Deep Learning Based Contactless Authentication System Using Hand Gestures

1. Deep Learning Based
Contactless Authentication System Using Hand Gestures
Mini Poject
SUDEESH V S
ROLL NO:844
SUBMITTED BY

2. Abstract
This work presents the design and implementation of a complete end-to-end deep learning based
edge computing system that can verify a user contactlessly using ‘authentication code’. The
‘authentication code’ is an ‘n’ digit numeric code and the digits are hand gestures of sign language
digits.

3. edge computing
a part of a distributed computing topology in which information processing is located
close to the edge
memory-efficient CNNs
a lesser number of parameters and have low computational complexity.
Project overview

4. Dataset
ZEYNEP DIKLE, A.M.; STUDENTS, T.A.A.A.H.S. SIGN LANGUAGE DIGITS DATASET. 2017
Figure 1. Sample images of the dataset

5. The edge computing system consists of two steps, it first takes input from the camera attached to it in real-time and
stores it in the buffer. In the second step, the model classifies the digit by taking the first image in the buffer as
input.A
Working

6. Flow diagram of the proposed deep learning mode

7. Figure 2. proposed end to end system
End to end system

8. Predictions

9. Reference Paper
DESIGN AND IMPLEMENTATION OF DEEP LEARNING BASED CONTACTLESS AUTHENTICATION SYSTEM USING HAND GESTURES
BY
AVEEN DAYAL 1 , NAVEEN PALURU 2 ,
LINGA REDDY CENKERAMADDI 1, * ,
SOUMYA J. 3 AND PHANEENDRA K. YALAVARTHY 2
PUBLISHED
AT
MDPI ON 15 JANUARY 2021

10. THANK YOU