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CSE499B EVM Team Project
- 2. Team Members Mahmudur Rashid Aurko •1511966042 Md. Aowrongajab Uaday •1512918043 Md. Nazmul Islam Suzan •1511783643
- 5. Working Procedure of our EVM System
- 6. This Machine will check the voter belongs to the voting center or not. If not, then the machine will reject the voter. If yes, then the Machine-2 will be activated automatically and Machine-1 will be deactivated. The camera will start recording this time.
- 7. Machine-2 is to check the fingerprint of the voter. To check and verify the voter, this machine is introduced. If the fingerprint matches, then the Machine-3 will be activated and Machine-2 will be deactivated.
- 8. After deactivating Machine-2, Machine-3 will be activated to input the vote for the voter.
- 9. Voting Counter will be set up separately from the three machines. It will just count the votes.
- 10. This is the important part of this EVM system. This will give the actual identity of the voter.
- 11. Equipments Machine-1 •RF Reader & RF ID •Arduino Nano •Power Supply Machine-2 •Fingerprint Sensor •Arduino Nano •Power Supply Machine-3 •Arduino Mega •Switch Setup •Power Supply Machine-4 •Arduino Nano •Key Pad •Power Supply Camera •USB Camera •Power Supply Voice •Arduino Nano •Mini Speaker •Storage •Power Supply Others •Lighting •Wires •Related Circuits •Graphical Interfaces
- 12. Image Processing Algorithms • Viola-Jones (Face Detection) • Speed Up Robust Feature(SURF) (Face Recognition) • Local Binary Patterns(LBPH)
- 13. Image Processing Flow Chart
- 14. Implementation A training set is Captured(for each person 8 images) Stored it in a folder Detect and extract match Minimum error between detected and training set Labels are inserted to indicate the name of that person
- 15. More Detailed Step-1 a). Matlab support package for USB Camera b). Introduced a face detector Step-2 a). Capture a snapshot b). Convert it grayscale c). Preprocess it (100*100 matrix)
- 16. Step-3 a). Detected face in capture image b). Verifying that there is exactly one detection. c). And that is above size of threshold (bbox size=30) Step-4 ( Quality Control Checks) a). Make sure that the face contains “one mouth and one nose”. b). Auto crop the detected face c). Preprocess it as desired and resize it to a standard size d). Stored it an auto- created directory
- 17. Step-5 Repeat step 3 to 5 until 8 images are captured for each person Step-6 Slurp in all captured face image and train detector using SURF
- 18. Training Algorithm Aggregated feature from stacked images Part-1 a). Create a stacked image montage of 5 randomly selected training image Montage image look like this:
- 19. Part-2 a). Run a feature detector and detect and extract b). Store feature in from each montage we trained using SURF SURF feature extraction Part-3 a). Now we have a set of feature b). Calculated for each face ,we can use it as a classifier.( this should have taken only few second )
- 20. Best Prediction Part-4 a). Restart the streaming image capture b). Detect face c). If multiple face detected then live Streaming will be OFF instantly c). Crop each face d). Apply detect and extract feature in each e). Match with training set which most closely is called “Best Prediction”
- 21. Best Prediction SURF feature matching Part-4 a). Restart the streaming image capture b). Detect face c). If multiple face detected then live Streaming will be OFF instantly c). Crop each face d). Apply detect and extract feature in each e). Match with training set which most closely is called “Best Prediction”
- 22. Thank You… Mail me for Question- mahmudur.rashid@northsouth.edu