Eye tracking to enhance facial recognition algorithms

Balu Ramamurthy Brian Lewis December 15, 2011

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Facial recognition is growing security concern

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Best recognition algorithm is human brain

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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

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Biometrics Background Eye Tracking Experiment Facial Recognition Experiment Facial Recognition Results Conclusion Future Work

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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

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Used 10 males and 10 females

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Ran identification and verification experiments

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Females much better at identifying faces

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Conducted identification and verification experiments

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2 Normalized faces shown to participant Participant asked to say if same person or different person

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Participant looks at image of face for as long as needed. Then shown 2 by 3 grid of normalized faces to identify correct face

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Each correct image broken up in to 7 by 7 grid

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Percentage of fixations for each block extracted.

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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

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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

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Develop algorithm to properly weight boxes

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Look at using new tasks for eye tracking

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Try new facial recognition algorithms on data

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Run experiments using specific facial regions