Biometric Facial Recognition

Biometric Facial Recognition
When determining the perpetrator of a crime, fingerprint identification and biometric facial
recognition are commonly used forensic methods. Fingerprint identification, which is defined by
Thomas E. Baker (2016) as "the matching of fingerprint patterns found on surfaces to those of
known persons" (Thomas E. Baker, 2016), has long been an accurate way of identifying persons
since the early 20th century. Biometric facial recognition on the other hand, is a method of
identification which has come into fruition of late due to advances in modern computing and
imagery technology. Biometric facial recognition is defined by Joseph Dewey and the Salem Press
Encyclopedia (2013) as "computer applications that can identify a person using a digital image of
his or her face and comparing it to a database of stored facial images" (Dewey et al., 2013). When
comparing, and contrasting these two forensic methods of ... Show more content on Helpwriting.net
...
The process involves establishing a database of known fingerprints, which were stored based on
employment needs, criminal processing, or voluntarily. To identify a person based on fingerprints,
the technician uses the analysis of fingerprint minutiae, such as ridge endings, dots, islands,
bifurcations, and other features, for the specific identification of a person (Thomas E. Baker, 2016).
The process of making an identification based off the method of facial recognition is far more
advanced. Facial recognition measures distinguishing facial features such as the dimensions of the
nose, the distance between eye sockets, the slope of the forehead, the line and size of the jaw, and
the contours of the cheeks (Dewey et al., 2013). Once the subjects photograph is obtained by the
specific system, it uses the above parameters to compare the subjects photograph to one which had
been previously taken and stored in the organizations
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Retinal Recognition And Biometrics
Retinal scanning: Like DNA each human retina is unique. Its complex structure makes harder to
bypass for intruders.
Unique pattern of a user retina is mapped earlier into system, then a low perceived beam of light is
casted on the eye to map and match with the stored retinal mapping of that user
Iris recognition: Iris is a thin circular structure in the eye. Infrared rays used to perform Iris
recognition. It's used to identify an individual by analyzing the unique pattern of the iris from a
certain distance. Iris of every human are different from each other. Iris recognition biometrics is
contactless, which makes it the most hygienic biometric modality
Fingerprint:
Fingerprint is widely used as it's cheap. The three basic patterns of ... Show more content on
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It works on the basis of 'tickets'. The client authenticates itself to the Authentication Server which
forwards the username to a key distribution center (KDC). The KDC issues a ticket–granting ticket
(TGT), which is time stamped and encrypts the secret key and returns the encrypted result to the
user's workstation. This is done infrequently, typically at user logon; the TGT expires at some point
although it may be transparently renewed by the user's session manager while they are logged in.
When the client wants to communicate with another node, then it needs to send the TGT to the
ticket–granting service (TGS), which usually shares the same host as the KDC. The Service must be
registered at TGT with a Service Principal Name (SPN). The client uses the SPN to request access to
this service. After verifying that the TGT is valid and that the user is permitted to access the
requested service, the TGS issues ticket and session keys to the client. The client then sends the
ticket to the service server (SS) along with its service request.
SSL
Secure Sockets Layer (SSL) is one of protocol of authentication methods.SSL uses a cryptographic
system that uses two keys to encrypt data ,a public key known to everyone and a

The Use Of Facial Recognition Technology On Society
Introduction Developed in the 1960s, facial recognition technology has been used by the
government and companies to identify people by matching them to photos. The data for this
software was originally entered manually, limiting the scope of use. By 2001, the novelty of more
powerful facial recognition technology grasped the public's attention. During the January 2001
Super Bowl, surveillance cameras captured images to find people with a criminal record (FBI 2013).
This potential invasion of privacy under the pretense of public safety sparked a public debate about
what private information the government was allowed to take from us. So should we be putting
limitations on the use of facial recognition software in America in order to keep ... Show more
content on Helpwriting.net ...
In effect, people are unknowingly being put in suspect lineups without their awareness or consent.
This can lead to false accusations against innocent citizens. Most police departments still rely on
officers to verify that the suspect chosen by the face recognition software actually matches the
camera footage. However, humans shockingly make an error in this process once in every two cases
(Bedoya, Alvaro, et al 2016). In some instances, it is not only this human error that is leading to
their conviction but rather the system itself. A study, co–authored by the FBI, said that the facial
recognition software is actually less accurate when it comes to identifying African Americans.
Systems relying on mugshot databases have a disproportionate number of African Americans due to
their high arrest rates in America. This creates "racial biased error rates," that perpetuate implicit and
systemic racism in our society (Bedoya, Alvaro, et al 2016). But for American citizens in general, in
any crime being solved by using face databases, anyone is a potential suspect. Because of this,
regulations should be put in place to limit the use of this technology to cases where its use is
relevant and vital to solving the issue.
Social Media Another common and inappropriate use of facial recognition technology exists in
many social media platforms and systems. In the current social media domain,

Annotated Bibliography : Biometric Gait Recognition
Among the most recent interesting topics in biometric research,Biometric gait recognition is one.
GAIT is a relatively biometric modality which has an important advantage comparing with other
modalities, such as iris and voice recognition, in that it can be very effortlessly captured from a
distance. While it has recently become an interesting topic in biometric researches, there was little
investigation into Spoofing attacks of GAIT where if a person tries to imitate the dressing style or
way of walking of someothers. There was an experiment which recently scrutinized for the first time
the effects of spoofing attacks upon silhouette based gait biometric systems and prooved that it was
very indeed possible to spoof gait biometric systems by dressing impersonation and the deliberate
by choosing a target that has a similar build to the attacker.
Introduction: Biometric GAIT recognition means recognizing the people by the way how they walk.
And the primary most important thing towards avoiding unauthorized access is user authentication.
User Authentication:
User authentication is the process of verifying claimed identity. Conventionally, user authentication
is classified into three different classes:
1. Knowledge – based: The knowledge–based authentication is nothing but something one knows
and is featured by secrecy. The examples of knowledge based authentications are the passwords for
Computers and PIN codes for Mobile Devices and etc, which we use in our daily life.

Facial Recognition Pros And Cons
Facial recognition is a technological product of the digital age that is continuing to evolve. The
efficiency of the technology is an asset to crime detectives apprehending criminals, or the
government tracking suspected terrorists for the safety of the country. It can also be used on a
corporate level to allow a business to understand their ideal customer, or to increase the
intractability of their website. While these abilities are positives to the people using them, the data
collection may be harming more than it is helping. Considering the benefits of facial recognition, it
is important for people to see what they are giving up for a more efficient experience. Facial
recognition technology forces users to relinquish control of their own ... Show more content on
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Over the course of this past year, body cameras are becoming increasingly popular with police
officers in the United States. This is in response to the public's outrage with unlawful police force.
Recently, companies that manufacture such bodycams announced plans to include facial recognition
software. The data collected from the cameras would be in the hands of the police for a minimum of
18 months, depending on the incident recorded (Chapman). While this could initially be seen as a
positive method to identifying and arresting an active suspect, there is a question of privacy of
control when it comes to this matter. This is an invasion of privacy for the citizens caught on camera
that are not involved in an incident nor have a criminal record. These cameras are often worn during
protests, both peaceful and not, capturing everyday citizens speaking their thoughts and ideas. A
concern that exists is if the police will use the data collected from the cameras to unlawfully monitor
participants (Chapman). This would inevitably discourage participation in these gatherings and take
control from peaceful citizens and give too much control to police officers. There is also no
guarantee that the footage will not be used for other purposes that the public is not yet aware of.
"There is no U.S. federal law at the moment that directly protects the privacy of American citizens
from the potential surveillance using facial recognition," (Chapman). The fact that there is no
balancing system for the rights of United States citizens may lead law enforcement to take
advantage of their freedom and use the technology inappropriately and excessively. The
development of this technology is growing so quickly that legislature has not accounted for the

Image Processing and Recognition
Preprocessing
It required a lot of study on previous work and some study of related topics which uses a little bit
same technique to process their features for recognizing a person. Most of the literatures were based
on palm image processing and some of the books and research papers were based on face
recognition. As such, a lot of research work has been done by Chinese researchers so far. A series of
researches gave me a set of methods and a set of features to be selected among them.
This research also requires depth knowledge of image processing. Fetching out each position of the
palm image through the reading it pixel by pixel. Some kinds of reading patterns of image should be
crystal clear in the mind while working with the features of this topic.
There are various useful books also available on image processing provides knowledge of different
methods. Which one would be better, could be decided after discussion with my guide.
1. Document Representation
On the basis of the study of previous researches, I had a great collection of previous research papers
(published by others) and related books. Before getting to start with the feature selection, document
representation has been done with the help of power point presentation. This includes a systematic
approach to represent this research work.
This gets start with the introduction, overall structure of the topic, previous researches done by
others, reason behind my research, introduction of new features

Persuasive Speech : Automatic Speech Recognition
Automatic speech recognition is a tool that allows computers to translate spoken language into
written text. This technology can assist users in interpreting and using audio information for
applications such as transcribing interviews, human–computer interactions, and many more. Speech
recognition is an application the Ministry of Justice has expressed great interest in. They wish to
automate the conversion of voice recordings of inmate phone conversations to text, which can then
be analyzed for safety critical information. Currently, employees take time out of their regular duties
to transcribe the audio manually. This takes a considerable amount of time that could be redirected
to other important duties and is why a high functioning ... Show more content on Helpwriting.net ...
These HMMs are used by the decoder to match audio features with a specific phoneme. The decoder
keeps track of the set of phonemes found until the audio reaches a pause, and then the system
searches the dictionary to construct a set of words that match the phonemes recognized. The
dictionary is simply the set of words to be recognized and the phonemes that make them up. The
language model is used in this process as well. Since the language model contains the probabilities
of words and word sequences it is used to constrict the dictionary search to words that are more
probable to occur [3]. Therefore, the word or words constructed with the highest probability are then
the next words in the transcript [4]. Figure 1 outlines the high level process of a speech recognition
engine.
This research project for the Ministry of Justice started with looking into the CMU Sphinx
automatic speech recognition software system. In particular Sphinx4 which is an open source
software system that is easy to supply different knowledge base models to tailor the recognition
results [6]. However, the baseline accuracy of the Sphinx4 system was less than desirable. The
baseline system, using the models provided, had an average WER of 81.70% over ten inmate test
files.
To improve on this, the first project was to research the benefits, if any, to modifying the provided
models to incorporate inmate

Finger Print Recognition and Image Enhancement Using Matlab
A PROJECT REPORT
ON
"FINGERPRINT RECOGNITION AND IMAGE ENHANCEMENT USING MATLAB"
Submitted in partial fulfillment
Of the requirements for the award of the degree in
BACHELOR OF TECHNOLOGY
IN
APPLIED ELECTRONICS AND INSTRUMENTATION ENGINEERING
SUBMITTED BY:
SHAKTI ABHISHEK– 0803112
SATISH GOYAL – 0803064
ROHIT DASH – 0803086
MD. IRFAN ARIF RAHMAN – 0803117
[pic]
DEPARTMENT OF APPLIED ELECTRONICS AND INSTRUMENTATION
ENGINEERING
GANDHI INSTITUTE OF ENGINEERING AND TECHNOLOGY
Biju Patnaik University of Technology, Odisha.
GUNUPUR, RAYAGADA DISTRICT, ODISHA, INDIA.
2011–2012
GANDHI INSTITUTE OF ENGINEERING AND TECHNOLOGY
GUNUPUR, RAYAGADA DISTRICT, ODISHA, INDIA.
[pic]
DEPARTMENT OF APPLIED ELECTRONICS AND INSTRUMENTATION
ENGINEERING ... Show more content on Helpwriting.net ...
The obtained image is applied to a thinning algorithm and subsequent minutiae extraction. The
methodology of image preprocessing and minutiae extraction is discussed. The simulations are
performed in the MATLAB environment to evaluate the performance of the implemented
algorithms. Results and observations of the fingerprint images are presented at the end.
LIST OF FIGURES
PAGE NO.

Figure 1.1: Sample fingerprint image 2
Figure 1.2: Types of minutiae 3 Figure 1.3: Types of local ridge features 4 Figure 1.4: (a) Two
important minutia (b) Other minutiae features
Figure 1.5: Verification Vs Identification 4
Figure 2.1: Fingerprint scanning using optical sensor 5
Figure 2.2: Fingerprint scanning using capacitive sensor 8
Figure 2.3: Fingerprint scanning using ultrasonic sensor 9
Figure 3.2: Different steps

Top-Down Influences On Spoken Word Recognition
Discuss the evidence for top–down influences on spoken word recognition.
What is the best way of accounting for these effects?
Introduction
One fundamental issue in the study of spoken word recognition is concerned with how and when
bottom–up information (i.e., raw acoustic input) is integrated with top–down knowledge (i.e.,
lexical, morpho–syntactic and semantic information) when processing speech, if at all. Interactive
accounts, represented by the TRACE model, acknowledge top–down flow of information as one
essential route within its theoretical portrait of a bi–directional spread of activation when processing
spoken words, as in contrast to autonomous ones, such as the SHORTLIST B model, which endorse
a unidirectional bottom–up approach. The paper discussed both positive and negative empirical
evidence for top–down effects in speech perception following a chronological order in line with two
influential theoretical accounts (i.e., Interactive versus Autonomous) to shed light on a more
comprehensive understanding of spoken word recognition. In particular, this discussion aims to
argue that the role for top–down influences on spoken word could be better consolidated
empirically, only when the idea of top–down flow is more rigorously conceptualized.
Early evidence
One long running approach to investigate how does higher–level information influence speech
perception focuses on testing if lexical knowledge influences phoneme perception through top–
down feedback, or

Face Recognition
Hi class,
You already mentioned some very interesting examples of where face recognition could be used or
already is in use. Authentication is needed more and more in a digitalized world. At first, I thought
face recognition could be useful for unlocking phones or open door locks, for example. But I came
to notice that there are situations in which this face recognition could actually be disturbing or even
dangerous. Imagine a person having an accident and somebody tries to call for help using the cell
phone of this person. Help could be hindered by a phone only unlocking through face recognition of
the owner. A similar situation would arise in a burning house. The owner being trapped inside,
firefighters may not be able to enter the house for help as quickly as they could without a face
recognition lock. ... Show more content on Helpwriting.net ...
Instead of using a credit or debit card with a pin number or signature, you could use face recognition
to identify the card owner. There is no situation I can think of in which somebody else would legally
have to use somebody else's bank card. Another positive aspect of face recognition in bank business
would be people not having to remember so many pin numbers. Many ATMs already have cameras
installed, so why not use those cameras for authentication? Even in stores cameras could be used to
identify people. The same applies for bank counters or online banking services. Especially the topic
of online credit card fraud would be interesting for using face recognition, as a lot of fraud is
committed over the anonymity of the

Literature Review On Speech Recognition
ABSTRACT
This paper provides a literature review of Speech Recognition, its background, methodologies and
techniques used. Speech to Text or ASR involves processing the sound waves, extracting basic
linguistic units or phonemes [1], then creating contextually correct and meaningful words to form a
complete sentence. This paper explains types of speech, different approaches for speech recognition,
techniques, the extraction of characteristics and the mathematical representationof ASR. Keywords
Speech Recognition, modules/phases of ASR, HMM, feature extraction, MEL, LPC, Large
Vocabulary Continuous Speech Recognition–LVCSR, Pattern Classification techniques,
Applications, tools
INTRODUCTION
The most common and primary communication method ... Show more content on Helpwriting.net ...
It effects the complexity, processing requirements and accuracy of the recognition. There are no
defined standards for size, the general perception is; small vocabulary – tens of words medium
vocabulary – hundreds of words large vocabulary – thousands of words very–large vocabulary –
tens of thousands of words. Types of Speech Recognition Systems
It can be categorized as follows; ISOLATED WORDS:
In this type of recognition, each word is surrounded by pause or break. It accepts single words or
single utterance [3] at a time. These systems have "Listen/Not–Listen" states, where they require the
speaker to wait between utterances (usually doing processing during the pauses) [2].
CONNECTED WORDS:
It's similar to isolated words, but allow separate utterances to run together with minimal gaps
between them.
CONTINUOUS SPEECH:
Words run into each other and have to be segmented. [4]
SPONTANEOUS SPEECH:
It recognizes the natural speech. Such system is able to handle features of natural language as
spontaneous speech may include mispronunciation, false starts and slang

Automatic Face Recognition Essay
Automatic face recognition has always been a major focus of research for a few decades, because of
numerous practical applications where human identification is needed. Compared to other methods
of identification (such as fingerprints, voice, footprint, or retina), face recognition has the advantage
of its non–invasive and user friendly nature. Face images can be captured from a distance without
interacting with the person, which is particularly beneficial for security and surveillance purposes.
Furthermore, extra personal information, like gender, face expressions or age, can be obtained by
further analyzing recognition results. Nowadays, face recognition technology has been widely
applied to public security, person verification, Internet ... Show more content on Helpwriting.net ...
Therefore, the original image space is highly redundant, and sample vectors could be projected to a
low dimensional subspace when only the face pattern are of interest. A variety of subspace analysis
methods, such as Eigen Face~cite{turk1991eigenfaces},
Fisherface~cite{belhumeur1997eigenfaces}, and Bayesian
method~cite{moghaddam2000bayesian}, have been widely used for solving these problems. One
of the most useful methods is Mutual Subspace Method (MSM)~cite{yamaguchi1998face}. MSM
is an extension of Subspace Method~cite{smguide:2007} and is based on estimation of multiple
face image patterns obtained under changes of facial expressions, face directions, lighting and other
factors. In MSM, two sets of patterns to be compared are represented by different linear subspaces
in a high–dimensional vector space, respectively. Each subspace is generated by applying PCA to
the set of patterns. It works pretty well in most cases. However, it is commonly known that
traditional PCA is not robust in the sense that outlying data can arbitrarily skew the solution from
the desired solution. The problem is that PCA is optimal in a least–squares sense. By the traditional
PCA, only the few first components are kept, supposed to preserve most of the information
expressed by the data. If the dataset contains too many noisy vectors, the principal components will
encode only the variation due to the

The Importance Of Word Superiority On Word Recognition
The purpose of this essay is to evaluate the effects of word frequency and superiority on word
recognition when considering three models of lexical access – with these being the logogen,
frequency ordered bin search (FOBS) and TRACE models. Included in this analysis empirical proof
from research studies will be considered. It is vital to understand the relevance of the word
frequency and superiority effects before contemplating their role in the models of lexical access.
Word frequency is often associated with Balota and Chumbley (1984) as their research, comprising
of three experiments using 20 participants, demonstrates that the time taken to respond to a high
frequency, commonly occurring words was shorter than that of a low frequency when recognising
words. Contrasting this, Mosell, Doyle and Haggard (1989) observed this effect on a lexical decision
and then a semantic categorisation task. Their findings demonstrate that word frequency and
response time are equally related in all tasks involving word identification. Balota and Chumbley
(1990) responded to the criticism as they remain confident in their own findings supporting the view
that word frequency demonstrates that words we encounter on a daily basis require a shorter
response time. The purpose of the word superiority effect is explored in Reicher's (1969) study
which used 9 female students. After being extensively tested via forced–choice letter recognition on
ability to recognise varying stimuli (4 letter words

The Human Face Action Recognition System
Abstract– In this paper we implement the Human Face Action Recognition System in Wireless
Sensor Network. Detecting movements of human is one of the key applications of wireless sensor
networks. Existing technique is detecting movements of a target using face tracking in wireless
sensor network work efficiently but here we implementing face action recognition system by using
image processing and algorithms with sensors nodes. Using sensor node we can collect the
information, data about human facial expressions and movements of human body and comparing old
data captured by sensors to the new capturing data, if data is match then we can say that detecting
human is same as early. Here we create new framework for face tracking and its movements
capturing, achieve tracking ability with high accuracy using Wireless Sensor
Networks. We use the Edge Detection Algorithms, Optimal Selection Algorithm, Image Processing
Technique, Action Recognition, the big data analysis. Using java language, various types of sensors.
Keywords– Mobile Network, Ad–hoc Network, Routing Protocol, Sensor Networks, Surveillance
system, Pattern Recognition.
I. Introduction Face Recognition is a technology to extract facial features by computer and a
technique for authentication according to the characteristics of these features. Face Recognition

Facial Recognition System
CHAPTER 1 FACIAL RECOGNITION SYSTEM
1.1 INTRODUCTION
A biometrics is, "Automated methods of recognizing an individual based on their unique physical or
behavioral characteristics." For example face, fingerprint, signature, voice etc. Face recognition is a
task humans perform remarkably easily and successfully.
In face recognition Features extracted from a face are processed and compared with similarly
processed faces present in the database. If a face is recognized it is known or the system may show a
similar face existing in database else it is unknown.
Face recognition has been a topic of active research since the 80's, proposing solutions to several
practical problems. It has been a challenging job for the researchers to develop a facial recognition
system with 100% accuracy because of all the difficulties and limitations. As we know that the
human face changes after a short period of time so no facial recognition system can work perfectly
after a few years. It has to be updated with the latest database images of the person in order to verify
the person. Also wearing spectacles, mask, mustaches may also affect the output of the face
recognition system. Face recognition is a biometric which is much easier to understand, because we
recognize or identify different people mostly by their faces. However the recognition process used
by the human brain for identifying faces has not a concrete explanation. It has now become essential
to have reliable security systems in

A New World Of Revenue Recognition
Article Write–Up 1 Yeaton (2015) research titled "A New World of Revenue Recognition" about the
discussion of the new revenue recognition standard, jointly issued by FASB and IASB, which is
effective after December 15, 2016 for public companies and after December 15, 2017 for private
companies and non–profit organizations (p.50). Yeaton identified that the new revenue recognition
standard will supersede most, if not all existing revenue standards (p.50). Yeaton summarized the
purpose of the GAAP and IFRS converged standards on revenue recognition to provide consistent
guidance to replace or remove the transaction and industry or geographic specific guidelines, to
simplify or streamline current revenue criterion, and to enhance disclosure statement to demonstrate
the nature, timing, amount and uncertainty of cash flow and revenue (p.50). Yeaton implied that the
new standard of recognizing revenue will have significant impact on real estate and
telecommunications companies, however it will provide variable impact on all companies, and in
order to capture, to align and to justify business decision on revenue measurement, it could
potentially require substantial changes on its existing process, policies and frameworks (p.50).
Yeaton recognized the need of additional or frequent use of judgement and estimation to comply
with the new requirements under the new principles of revenue recognition (p.50). Yeaton (2015)
determined areas where companies would have to make necessary

Impact Of The New Revenue Recognition Standard
Impact of the New Revenue Recognition Standard
Internship Course
Caleb Cartledge
4/11/15
Change is on the horizon and many companies are scrambling trying to figure out how the New
Revenue Recognition Standard will impact the way that they conduct business. The prospect of
bracing for a game–changing revenue recognition standard at a larger global firm is a daunting task.
GE Technical Controller Russell Hodge, CPA, commented about this stating, "I'll admit to it being a
little bit overwhelming to us. We have $150 billion of revenue and so many diverse, different
business models. It's a tough question. It's a tough thing to think about."
(http://journalofaccountancy.com/Issues/2014/Mar/revenue–recognition–20149444.htm) With
experienced well–known firms such as GE being concerned, it shows that even the most
experienced companies are concerned with how the new standards will fundamentally change the
way that they conduct business. The main purpose of this essay is to explore how the new revenue
recognition standard will effect different industries when it comes to fruition in 2017. The secondary
purpose of this essay is to discuss ways that these different companies can mitigate risk by
effectively preparing for these changes by using preventative controls.
For readers who are unfamiliar with the topics discussed above, The Financial Accounting Standards
Board recently issued a new set of standards governing revenue recognition. The new standards will
take effect on

What Is Meant by State Recognition in International Law
What is meant by state recognition in international law INTRODUCTION Some definitions of
"international law" can be found on the Web as follows: "The body of laws governing relations
between nations", "International law is the term commonly used for referring to the system of
implicit and explicit agreements that bind together nation–states in adherence to recognized values
and standards, differing from other legal systems in that it concerns nations rather than private
citizens. ...", "A complex network of principles, treaties, judicial decisions, customs, practices, and
writings of experts that are binding on States in their mutual relations." The common point of the
definitions is that, international law deals with the states and ... Show more content on
Helpwriting.net ...
What are the rights and duties, the powers and immunities that attach to an entity because it is a
State? Crawford gives a list of five "exclusive and general legal characteristics" of States: "(1) States
have plenary competence to perform acts in the international sphere make treaties and so on. (2)
States are exclusively competent with respect to their internal affairs–"exclusive" means plenary and
not subject to control by other States. (3) States are not subject to international process without their
consent. (4) States are regarded in international law as "equal", it is a formal, not a moral or political
principle. (5) States entitled to benefit from the Lotus presumption, especially that any derogation
from the previous principles must be clearly established." These are the criteria and the
consequences of being a State, the next chapter will focus on to be accepted as a State. II.
RECOGNITION At the beginning of XX century, there were nearly fifty states in the world arena,
just before the World War II the number reached approximately seventy–five and in 2005, there
were almost 200. Each State creation, again and with some problems, put the recognition concept on
the agenda of international community. Recognition has become much important especially by
reason of its results. What is meant by recognition of States? Grant defines

General Accepted Accounting Principles ( Gaap ) : The...
Accounting Standards Update 2014–09 Revenue from Contracts with Customers (Topic 606)
ACCT 610 Financial Reporting and Disclosure
Daisy Gaytan
California State University, San Bernardino
Abstract
1. General Accepted Accounting Principles (GAAP): The Revenue Recognition Principle Revenue
recognition is a difficult matter in accounting. The company's results differ depending of the method
used to recognized earnings. This is a complex situation since business activities are broad and
intricate. Therefore, policies and regulations are imperative to reduce manipulation of financial
statements. The foundation of the income measurement is the accrual accounting. According to
Collins, Johnson, and Mittelstaedt (2012) under accrual accounting, revenue is recorded in the
period when they are earned and become measurable. This is when the vendor rendered a service, or
transferred an asset to the purchaser and the value of the transaction is realistically assured.
Expenses are also recorded in the same period in which the revenues are recognized (the matching
principle). Reported revenues under accrual accounting most of the time do not match cash receipts
during the period. Accrual accounting provide a more accurate measure of the economic value
added than cash flows. The accounting process of recognizing income includes two steps: revenue
recognition, and expense matching.
1.1 Criteria for Revenue Recognition According with GAAP, (Collins et al.)

Artificial Intelligence for Speech Recognition
ARTIFICIAL INTELLIGENCE FOR SPEECH RECOGNITION
THE FUTURE OF HUMAN EVOLUTION
ABSTRACT: When you dial the telephone number of a big company, you are likely to hear the
sonorous voice of a cultured lady who responds to your call with great courtesy saying "welcome to
company X. Please give me the extension number you want" .You pronounces the extension
number, your name, and the name of the person you want to contact. If the called person accepts the
call, the connection is given quickly. This is artificial intelligence where an automatic call–handling
system is used without employing any telephone operator. Artificial Intelligence (AI) involves two
basic ideas. First, it involves studying the thought processes of human beings. Second, it ... Show
more content on Helpwriting.net ...
Inevitably Artificial Intelligence has, and will continue to affecting our lives. Artificial Intelligence
(AI) Effort to develop computer–based systems:  that behave like humans:  learn languages 
accomplish physical tasks  use a perceptual apparatus With the development of practical
techniques based on AI research, advocates of AI have argued that opponents of AI have repeatedly
changed their position on tasks such as computer chess or speech recognition that were previously
regarded as "intelligent" in order to deny the accomplishments of AI. They point out that this
moving of the goalposts effectively defines "intelligence" as "whatever humans can do that
machines cannot". A speech recognition system is a type of software that allows the user to have
their spoken words converted into written text in a computer application such as a word processor or
spreadsheet. The computer can also be controlled by the use of spoken commands. Speech
recognition software can be installed on a personal computer of appropriate specification. The user
speaks into a microphone (a headphone microphone is usually supplied with the product). The
software generally requires an initial training and enrolment process in order to teach the software to
recognize the voice of the user. A voice profile is then produced that is unique to that individual.
This procedure also helps the user to learn how to speak to a computer.
WORKING: The user speaks

Biometric Recognition
In biometric recognition there different kind of recognition that relates to biometric but some of the
current uses in iris recognition is the national border controls which is the iris being an passport.
Also their more current uses for the iris recognition is the forensic usage which is when tracing and
finding a missing person or any individual that was missing personal files. There also other uses that
their using and one of them is for iris recognition is using the internet security to control the access
of the privileged information. Some of the current uses for face recognition is for crime fighting
which is when the law enforcement agents are using to recognize many people based on their face.
Also another uses of face recognition

Fasb's New Revenue Recognition Guidelines
My topic is FASB's new revenue recognition guidelines. Revenue could not be recognized under the
old model unless the seller had substantially met the terms of the agreement, the seller had delivered
the goods or performed a substantial part of the service, the risks and rewards of ownership had
passed to buyer, Revenue is the largest item in financial statements, and issues involving revenue
recognition are among the most important and difficult that standard setters and accountants face.
Revenue recognition requirements in U.S. GAAP are different from those in IFRSs and both are
considered in need of improvement. U.S. GAAP comprises broad revenue recognition concepts and
numerous industry or transaction–specific requirements that can result in different accounting for
economically similar transactions. Although, IFRSs contain less guidance on revenue recognition,
its two main standards IAS 18 Revenue and IAS 11 Construction Contracts can be difficult to
understand and apply beyond simple transactions. Also, they lack guidance on important topics such
as revenue recognition for multiple–element arrangements. Reporting inconsistencies in this
accounting area arise because there are no comprehensive accounting standards covering revenue
recognition. Several authoritative accounting pronouncements have addressed detailed, industry–
specific revenue recognition issues which caused practitioners to use standards in situations for
which they were not intended. As a result, in

Biometrics And The Black Market For Recognition Purposes
Just imagine walking into a GAP clothing store as a device measures a person's eyes for recognition
and then proceeds to deliver custom content on items previously purchased. In the futuristic sci–fi
thriller Minority Report a scene, exactly as described, unfolds. The story was set in Washington D.C.
in the year 2054. While the film was filled with hero's, hacker's, and drama, a city–wide optical
recognition system was used to identify its citizens. Minority Report gave an interesting perspective
on how biometrics might work in the future for recognition purposes. It also sparks the imagination
on what other forms of biometrics could be used to identify individuals. Is a journey to the black
market for a dicey eye transplant in our future?
Biometrics is a term that refers to the broad amassment of various human characteristics. In
computer science, biometrics authentication is used for access and a form of identification.
Biometrics can also be used to survey and identify individuals in groups. Statistically, biometrics are
unique and measurable to only one individual. When biometrics are used in authentication they can
be broken down into two groups of measurability, physiological characteristics and behavioral
characteristics. Physiological characteristic can be, but are not limited to, Biometrics that consist of
a person's Deoxyribonucleic acid (DNA), fingerprints, face, hands, eyes, ears or even odor. The
second of the two characteristics, are the behavioral

Strengths And Weaknesses Of Speech Recognition
CHAPTER 1
INTRODUCTION
People use calculators to perform calculations that have a numeric keypad to input the data and a
display to get the result. To overcome this, speech signal can be used to input the data and the result
is an audio signal.
Speech is the vocalized form of human communication. It ranges from 90 Hz to 7,000 Hz. Each
spoken word is created out of the phonetic combination of a limited set of vowel and consonant
speech sound units. The voiced speech of a typical adult male will have a fundamental frequency
from 85 to 180 Hz, and that of a typical adult female from 165 to 255 Hz. Thus, the fundamental
frequency of most speech falls below the bottom of the "voice frequency" band as defined above.
Speech recognition is a process ... Show more content on Helpwriting.net ...
Generally the more traing, the higher the perfromance of the system for virtually any task. The
reference pattern are sensitive to the speaking environment and transmission characteristics of the
medium used to create the speech, this is because the speech spectral characteristics are affected by
transmission and background noise. No speech–specific knowledge is used explicitly in the system;
hence the method is relatively in sensitive to choice of vocabulary words, task, syntax, and task
semantics. The computational load for both pattern training and pattern classification is generally
linearly proportional to the number of patterns being trained or recognized. Hence computation for a
large number of sound classes could and often does become prohibitive. Because the system is
insensitive to sound class, the basic techniques are applicable to a wide range of speech sounds,
including phrases, whole words and sub word units. The basic set of techniques developed for one
sound class (e.g., words) can generally be directly applied to different sound classes (e.g., subword
units) with little or no modifications to the

The Pros And Dangers Of Recognition Advertising
The world is intergraded many different techniques to better market their product such as radio
frequency identification devices and facial recognition software. The ability to track via radio waves
with a small microchip that's placed on an item with the capability of being scanned with a
particular device is a radio frequency identification device or RFID (Ferrell & Hartline, 2014).
Researchers believe that facial recognition is just a simple measurement of point across that face
that will compile into a set pattern for an individual (Hecht, 2014). In my opinion, facial recognition
advertising would pose more of an invasion of privacy than the RFID. Facial recognition can happen
in many forms such as security cameras which can be in stores, in–home personal systems, phones,
and government cameras are just a few. As far as an invasion of privacy for facial recognition is not
unreasonable in its entirety however in the use of marketing in a store with cameras and using it for
add should be noted before someone shopping (Hudson, 2013). There is individual that are willing
going to buy the new iPhone X when it comes out who has the facial recognition as its primary
selling point; therefore, those individuals are accepting of the idea of their face being part of the
program. Furthermore, those cameras that are used in marketing for stores and billboards taking
pictures and facial recognition without customer's knowledge and having the potential of their
system to get hacked

Creating A Better Recognition System
ABSTRACT
Bangla word recognition is extremely challenging and there are too few works reported on online
cursive Bangla word recognition. Bangla is a complicated script and it needs rigorous investigations
to implement a better recognition system. While we have sophisticated classifiers like Hidden
Markov Models or BLSTM Neural Networks for recognition of complicated scripts, we have very
little comparative studies about the appropriate feature sets for such scripts. In this paper, our aim is
to implement an appropriate recognition system for author–independent unconstrained Bangla word
where a modified feature set is proposed. To construct the modified feature set, we modify existing
feature sets and add new features for improvement of recognition accuracy. We test performances of
various existing feature sets and proposed feature set on same Bangla dataset for fair comparison
and report the comparative results using various lexicons upto 20,000–word lexicon. Each time an
HMM–based classifier is used to test the feature set. Finally, a recognition system is built over the
combination of existing and modified feature sets.
Keywords: Online handwriting recognition, online character recognition, lexicon driven approach,
Bangla script, Indic script, cursive text recognition.
1. Introduction
Bangla text recognition is more challenging than Latin text recognition in several ways. Bangla
alphabet has complex shapes. There are more than 300 characters (including 50 vowel and

Optical Character Recognition Is Becoming Popular Areas Of...
Project Title:OPTICAL CHARACTER RECOGNITION USING ARTIFICIAL
NEURAL NETWORKS
Abstract: The Optical Character Recognition (OCR) is becoming popular areas of
research under pattern recognition and smart device applications. It requires the intelligence
like human brain to recognize the various handwritten characters. Artificial Neural Network
(ANN) is used together the information required to recognize the characters adaptively. This
paper presents a performance analysis of character recognition by Scale Invariant Feature
(SIF) matrix with a Back Propagation Neural network (BPN). A GUI based OCR system is
developed using Mat lab. The results are shown for the English alphabets and numeric. This
is observed that the perceptron network converges faster, whereas the BPN can handle the
complex script recognition when the training set is enriched.
Keywords:
Character recognition, back–propagation neural network, scale invariant feature
Introduction:
Automatic character recognition is a well–accepted area of research under pattern recognition.
In handwritten character recognition, characters are written by different individuals that vary
drastically from person to person due to variation in the writing style, its size and orientation
of characters. This makes the system difficult to recognize the characters. Artificial Neural

Network (ANN) helps to solve the problem of identifying handwritten characters in an
automated manner. ANN is an adaptive

Speech Recognition Essay
Speech Recognition Nowadays, computer systems play a major role in our lives. They are used
everywhere beginning with homes, offices, restaurants, gas stations, and so on. Nonetheless, for
some, computers still represent the machine they will never know how to use. Communicating with
a computer is done using a keyboard or a mouse, devices many people are not comfortable using.
Speech recognition solves this problem and destroys the boundaries between humans and
computers. Using a computer will be as easy as talking with your friend. Unfortunately, scientists
have discovered that implementing a perfect speech recognition system is no easy task. This report
will present the principles and the major approaches to speech ... Show more content on
Helpwriting.net ...
Increasing the number of words isn't enough because the speech recognition system is unable to
differentiate words like 'to' and 'two' or 'right' and 'write' (6 ,p.98). Use of Grammar Using grammar,
differentiating words like 'to' and 'two' or 'right' and 'write' is possible. Grammar is also used to
speed up a speech recognition system by narrowing the range of the search (6,p.98). Grammar also
increases the performance of a speech recognition system by eliminating inappropriate word
sequencing. However, grammar doesn't allow random dictation which is a problem for some
applications (6, p.98). Continuous vs. Discrete Speech When speaking to each other, we don't pause
between words. In other words, we use continuous speech. However, for speech recognition
systems, there is difficulty in dealing with continuous speech (6, p.98). The easy way out will be
using discrete speech where we pause between words (6, p.100). With discrete speech input, the
silent gap between words is used to determine the boundary of the word, whereas in continuous
speech, the speech recognition system must separate words using an algorithm which is not a
hundred per cent accurate. Still, for a small vocabulary and using grammar, continuous speech
recognition systems are available. They are reliable and do not require great computational power
(6, p.100). However, for

Literature Review On Speech Recognition System
II. LITERATURE SURVEY
Review of literature on speech recognition systems authentically demands consideration towards the
finding of Alexander Graham Bell regarding the method of converting sound waves into electrical
impulses and the first speech recognition system developed by Davis et al. [6] for finding telephone
superiority digits spoken at normal speech rate. This attempt for automatic speech recognition was
mainly centered on the edifice of an electronic circuit for discovering ten digits of telephone
superiority. Spoken words were examined to obtain a 2–D plot of formant 1 vs. formant 2. A circuit
is developed for finding the greatest correlation coefficient among a set of novel incoming data for
pattern matching. These features are grouped into feature vectors that serve the purpose of reducing
dimensionality and redundancy in the input to the speaker recognition system. An indication circuit
was created to exhibit the already discovered spoken digit. The proposed approach lays stress on
recognizing speech sounds and providing appropriate labels to these sounds. Various approaches and
types of speech recognition systems came into existence in last five decades gradually. This
evolution has led to a remarkable impact on the development of speech recognition systems for
various languages worldwide. The exact nature of the feature set depends on what part of a speech
signal the features are expected to represent and thus what type of information is to be extracted. In

The Visual Recognition Of Image Patterns
CHAPTER 1
INTRODUCTION
1.1. Background to the Study
As a scientific discipline, computer vision is concerned with the extraction of information from
images to be employed in a decision making process. The image data can take many forms, such as
video sequences, still images, digitized maps, diagrams and sketches. These images may be in
colour format, grey scale or in binary format. The common approach is to extract characteristic
features from the image either in the spatial domain or in some suitable transform domain. Whether
the goal is classification or recognition, a measure of similarity or distance must be formulated and
the success rate of the system evaluated. Some of the application areas of computer vision are:
1.1.1. Robotics
The visual recognition of image patterns is a fundamental human attribute that uses the eye as a
sensor and dedicated parts of the brain as the decision making processor. The visual recognition
enables humans to perform a variety of tasks such as target identification, ease of movement,
handling tools, communication among others. Advances in sensing and visual perception techniques
have enabled some of these attributes to be transferred to robots. For example, identification of
colour is a useful asset in a robot in an industrial automation process that involves sorting. [1], [2],
[3]
1.1.2. Industrial Inspection
Industrial inspection is an area in which pattern recognition is of importance. A pattern recognition
system captures images

Handwriting Recognition Essay
The one feature that got the most attention and strongly influenced its demise was its handwriting
recognition capability. In all message pads, handwriting recognition was the basis of data input to
many of the built–in applications and functions. According to Professor Luckie, this handwriting
recognition depends solely on Paragraph International Inc.'s Calligrapher recognition engine.
Calligrapher technology was limited by the dictionary of words to which it has access (Luckie, n.d.).
This shows that if you write a word that doesn't exist in the dictionary, then Calligrapher is not going
to recognize it correctly and often suggests funny but incorrect phrases as the user wrote.
Even though the limitation of the Calligrapher recognition partly contributed to the demise of
MessagePad, Apple was able to rescue Newton during the release of the second generation OS
(Newton 2.0 OS). This software brought the MessagePad back to the technology it was intended to
be by introducing the new printed handwriting recognition engine. Professor Luckie, in his website
that documents the evolution of Newton OS describes how the engineers at Apple had developed a
"letter by letter" handwriting reorganizer that could only recognize "printed" text (Luckie, n.d.). The
printed text recognizer in OS 2.0 requires no dictionary or handwriting learning period. It only
requires separated printed characters to analyze.
Both handwriting recognition engines were innovative systems that adapted to the

Face Recognition Essay
Seminar Report on
'Age–invariant Face Recognition'
Submitted by:–
Namit Ohri
(12213010)
Submitted to:–
Dr. Pyari Mohan Pradhan
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING INDIAN
INSTITUTE OF TECHNOLOGY ROORKEE – 247667
Abstract
A facial recognition system is a computer application capable of identifying or verifying a person
from a digital image or a video frame from a video source. One of the challenges in face recognition
is to come up with a matching model that is robust to effects and changes of facial aging. Aging
leads to both shape and textural changes in the face of a person. They(shape and texture) offer
important features for facial image classification related to the same individual and also
distinguishing between face images of different individuals.
In this report, we are considering digital facial images. We will discuss the various existing
techniques for extracting shape and texture features from a facial image and also techniques of
fusing these two feature sets together to obtain a higher accuracy. We will also review the existing
age–invariant facial recognition systems, their accuracies and limitations.
Introduction
The accuracy of a face recognition system depends on certain factors such as expression, lighting,
pose and age. But facial aging has not received enough attention as compared to the other three
factors. Aging affects both the shape and texture of a face. Before reaching the age of 18, a

Revenue Recognition
This paper provides an overview of the revenue recognition and highlights the criteria for companies
on when to recognize revenue. It also explains the reason why revenue recognition can be treated
differently in two circumstances within the same airline industry. Income measurement is typically
described as a two–step process consisting of revenue recognition followed by matching of expenses
to the recognized revenue. The criteria which guides the recognition of revenue clearly plays a
critical role in determining the outcome of the accounting process, and it is not surprising
recognition criteria are the focus of continuing debate (Antle & Demski, 1989). According to the
realization principle the income should be recognized when the earnings process is complete or
virtually complete, an exchange transaction has taken place and revenue is measurable (Schroeder,
Clark & Cathey, 2013). In other words, revenues ... Show more content on Helpwriting.net ...
The seller's price to the buyer is fixed or determinable 4. Collectability is reasonably assured.
(Philips, Luehlfing & Daily, 2001) In short, when a customer agrees to purchase a product or a
service and if the amount is already has been determined than the company can recognize revenue.
The reason why the revenue recognition is treated differently under these two different scenarios
with in the airline industry is because the service is completed in different phases. For example, the
travel agent should recognize revenue for its commission at the point the customer purchases the
ticket because earnings process is complete. In other words, the service that is provided by a travel
agent, which is helping the customer make travel plans and arrangements, is completed. Travel
agent is not responsible providing the flight as well. On the other hand, an airline recognizes its
revenue when the flight has been provided, as the services that are rendered by the airline include
not only upfront service with its own in–house sales force, but also the

The Preprocessing Phase Of Speaker Recognition Systems
The preprocessing phase in speaker recognition systems is used to give subsequent stages (features
extraction, modeling, and features matching) an intensification efficiency. Therefore, the system
performance is improved regarding classification rate and computing time. In this chapter, we define
the most important step in the pre–processing and we followed by analyzing the reason of chosen
algorithms and its impressions in a Speaker Identification System (SIS).
2.1 Pre–processing Structure gCommonly the pre–processing includes the sampling step,
windowing, and a denoising step. At the end of the preprocessing, the compressed and filtered
speech frames are forwarded to the feature extraction stage. The broad preprocessing pipeline is
shown ... Show more content on Helpwriting.net ...
A study of Sanderson & al [23] have exposed that a combination of feature vector size and a chosen
sampling frequency has a direct impact on system performance. Figure 4, present a wave file
sampled at f = 16 kHz.
2.1.2 Windowing & Frame Blocking
A term of quasi–stationary describes a slow variation in a given domain. In speech analysis, features
of an observed segment of a signal with a period Ts=(5–100 ms) could fairly be stationary. The
information in a speech signal is could be represented by short term amplitude spectrum
representation. The framing procedure is multiplying the speech wave with a windowing function.
The signal is weighted in the time domain (convolution) and divided into a sequence of partial
signals in the specter domain. By doing so, we improve time information of every partial signal.
In speech processing, There are various windowing functions, each with different shapes and
different characteristics. The form of the window function is not a critical factor. Usually, some soft
window like the Von–Hann, rectangular, hanning or hamming window [24] are used to decrease
incoherence of the speech signal at the boundaries of each frame segment.
In this work, a Hamming window is used and given by:
2.1.3 Silence & Background Noise Removal (Endpoint Detection):
Depending on the purposes of the speech processing technique, preprocessing could include Noise
Removal, Endpoint Detection, Pre–emphasis, Framing, Windowing, etc.

Artificial Intelligence For Speech Recognition
Name Magnus Oforji 1. Title Artificial Intelligence for Speech Recognition 2. Introduction:
Research Context from siri to self–driven cars, artificial intelligence is on a rapid progression. While
science fiction often portrays or visualises artificial intelligence as robots with human–like
characteristics, artificial intelligence can encompass anything from google search algorithms to IBM
Watson's to Autonomous weapons. Artificial astuteness (AI) for verbalization apperception involves
two fundamental conceptions. First, it involves studying the noetic conception processes of human
beings. Second, it deals with representing those processes via machines (like computers, robots,
etc.). Artificial intelligence is a behaviour of a machine, ... Show more content on Helpwriting.net ...
4. Objectives and Hypotheses The sole objective of this research will be to convert or transmit
spoken words into written text through a computer software application, and to also control the
computer application through words or spoken commands. The software should have the capabilities
of recognising a spoken word and understanding it to be able to provide coherent response. There
are two main objectives of focus for this research stated below: O1: Speech Recognition and speech
voice recognition: The software application should be able to perform both of this task stated above,
each use recordings of the human voice, but they do various things with it. Speech recognition strips
out the non–public variations to locate the phrases. Voice recognition commonly disregards the
language and meaning in other to detect the physical individual at the back of the speech. the key
distinction between voice recognition and speech recognition is that, the software doesn't understand
what you said, but it knows very well that it's you speaking. It can be expressed as a type of an
audible fingerprint. O2: Recognising a command in a speech: The software should also be able to
detect and recognise a command from a speech. 5. Research Methodology/Methods The speech
recognition process is generally performed by a component of the software which is known as the
speech recognition engine. The main function of this software known as speech recognition engine

Facial Recognition Software: The Invasion Of Privacy
Developed in the 1960s, facial recognition technology has been used by the government and
companies to identify people by matching them to photos. By 2001, the novelty of more powerful
and rapid facial recognition technology grasped the public's attention. During the January 2001
Super Bowl, surveillance cameras captured images of the crowd to find people with criminal records
(FBI 2013). This potential invasion of privacy under the pretense of public safety sparked a public
debate about the government taking private information from citizens. So what limitations should be
put on the use of facial recognition software in America in order to keep the public feeling secure?
In recent years, many companies who use this technology claim it is ... Show more content on
Helpwriting.net ...
Most police departments still rely on officers to verify that the suspect chosen by the face
recognition software actually matches the camera footage. However, humans shockingly make an
error in this process once in every two cases (Bedoya, Alvaro, et al 2016). In some instances, it is
not only this human error that is leading to false convictions but rather the system itself. A study, co–
authored by the FBI, noted that the facial recognition software is less accurate when identifying
African Americans. Systems relying on mugshot databases have a disproportionate number of
African Americans due to their high arrest rates in America. This creates "racial biased error rates"
that perpetuate implicit and systemic racism in our society (Bedoya, Alvaro, et al 2016). This
presents yet another reason regulations should be put in place solely to limit the use of this
technology to cases where its use is essential to solving a crime. Another common and inappropriate
use of facial recognition technology exists in many social media platforms and systems. In the
current social media domain, when you post or are tagged in a photo, you are, in essence, saying
goodbye to anonymity and the right to privacy that we value socially and culturally. Photos from
Facebook, Twitter, and Instagram can all be used online to create an unintended profile: your "face
print". Using this "face print",

Innovations in Handwriting Recognition Essay
Emergence networks mimics biological nervous system unleash generations of inventions and
discoveries in the artificial intelligent field. These networks have been introduced by McCulloch and
Pitts and called neural networks. Neural network's function is based on principle of extracting the
uniqueness of patterns through trained machines to understand the extracted knowledge. Indeed,
they gain their experiences from collected samples for known classes (patterns). Quick development
of neural networks promotes concept of the pattern recognition by proposing intelligent systems
such as handwriting recognition, speech recognition and face recognition. In particular, Problem of
handwriting recognition has been considered significantly during ... Show more content on
Helpwriting.net ...
The first study discusses the basic operations of erosion and dilation and present a system to
recognize six handwritten digits. For this purpose, a novel method to recognize cursive and
degraded text has been found by Badr & Haralick (1994 ) through using technology of OCR. Parts
of symbols (primitives) are detected to interpret the symbols with this method (KUMAR et al.
2010). The study involves mathematical morphology operations to perform the recognition process.
Cun et al. (1998) challenge this problem by designing a neural based classifier to discriminate
handwritten numeral. This study achieved a reliable system with very high accuracy (over 99%) on
the MINIST database. Moreover, the gradient and curvature of the grey character image have been
taken into consideration by Shi et al. (2002 ) to enhance the accuracy of handwriting digits
recognition. Uniquely, Teow & Loe (2002 ) identify new idea to solve this problem based on
biological vision model with excellent results and very low error rate (0.59%). The discoveries and
development have been continuing through innovating new algorithms and learning rules. Besides
efficiency of using these rules individually in the machine learning, some researchers have going
further in developing the accuracy and performance of the learning by mixing several rules to
support one

Recognition And Speech Recognition
Voice recognition or Speech recognition is an interesting topic now a days and it is blooming at it is
at peaks right now. Almost everyone is trying to incorporate this technology somehow in order to
make their product more effective to use and in order to make the product efficient.
Alternatively referred as speech recognition, voice recognition is a hardware device or a software
which has an ability to decode human voice. Voice recognition is commonly used in order to
perform command or write without using key board, mouse or pressing any buttons.
Applications of Voice/Speech Recognition: Voice recognition has various applications in various
field. Some of them are as follow:
Education: Through Voice recognition teachers can effectively creating online videos and lesson
tutorial for students while it is useful for learners to obtain necessary knowledge effectively and
quickly.
Writing texts: Writing text is a long process so does typing will take time so rather voice recognition
program will minimize writing effort and all that we need to do is text pronounciation correctly and
we can proof read it.
Security: Biometric Voice recognition software are actively used in ecommerce and banking because
it provides high level of security. Voice recognition became an effective solution for security for
various sector like frauds, telephone–crime and many others.
Medicine: Medicine is one area where they need effective and efficient work in order to collect
patient's information

Face Recognition Essay
SECURITY SURVEILLANCE SYSTEM
Security Through Image Processing
Prof.Vishal Meshram,Jayendra More
Department of Electronics & Telecommunication Engineering (ExTC), University Of Mumbai
Vishwatmak Om Gurudev College Of Engineering Maharashtra State Highway 79, Mohili,
Maharashtra 421601, India.
Vishalmmeshram19@gmail.com, more.jayendra@yahoo.in
Bhagyesh Birari,Swapnil Mahajan
Department of Electronics & Telecommunication Engineering (ExTC), University Of Mumbai
Vishwatmak Om Gurudev College Of Engineering Maharashtra State Highway 79, Mohili,
Maharashtra 421601, India. bhagyeshbb86@gmail.com, swapnilmahajan939@gmail.com
Abstract–Automatic recognition of people is a challenging problem which has received much
attention during recent years due to its many applications in different fields. Face recognition is one
of those challenging problems and up to date, there is no technique that provides a robust solution to
all situations. This paper presents a technique for human face recognition. A self–organizing
program is used to identify if the subject in the input image is "present" or "not present" in the image
database. Face recognition with Eigen values is carried out by classifying Eigen values in both
images. The main advantage of this technique is its high–speed processing capability and low
computational requirements, in terms of both speed, accuracy and memory utilization. The goal is to
implement the system for a particular face and distinguish it

Biometric Facial Recognition

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