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Eye tracking to enhance facial recognition algorithms. Balu Ramamurthy Brian Lewis December 15, 2011. Introduction. Facial recognition is growing security concern Best recognition algorithm is human brain Wanted to find a way to use brain information in recognition - PowerPoint PPT Presentation
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EYE TRACKING TO ENHANCE FACIAL RECOGNITION
ALGORITHMSBalu Ramamurthy
Brian LewisDecember 15, 2011
Introduction Facial recognition is growing security
concern
Best recognition algorithm is human brain
Wanted to find a way to use brain information in recognition
If we identify areas humans use to recognize faces, we can get unique results in algorithms
Contents Biometrics Background Eye Tracking Experiment Facial Recognition Experiment Facial Recognition Results Conclusion Future Work
Biometrics Background 2 types of biometrics, identification and
verification
Verification consists of confirming an identity
Identity comes from selecting correct person from a group of candidates
Current algorithms use features extracted from images
Eye Tracking Experiment Used 10 males and 10 females
Ran identification and verification experiments
Females much better at identifying faces
Conducted identification and verification experiments
Verification Experiment 2 Normalized faces shown to participant
Participant asked to say if same person or different person
Identification Experiment Participant looks at image of face for as
long as needed. Then shown 2 by 3 grid of normalized
faces to identify correct face
Eyetracking Heatmap
Facial Recognition Procedure
Each correct image broken up in to 7 by 7 grid
Percentage of fixations for each block extracted.
Facial Recognition Experiment
Experiment 1 gave each block equal distribution
Experiment 2 blocks weighted 0-3 with equal number of blocks in each weight
Experiment 3 blocks given weights of 0-4 based on fixation percentages
Experiment 4 only blocks of 100% fixation were used in algorithms
Facial Recognition Results
Conclusion No significant recognition rate
improvement
Blocks with 100% fixation account for 50% of accuracy
Trial and error in experiments 3 and 4 give hope for future work
Future Work Develop algorithm to properly weight
boxes
Look at using new tasks for eye tracking
Try new facial recognition algorithms on data
Run experiments using specific facial regions