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Eye tracking to enhance facial recognition algorithms

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

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Eye tracking to enhance facial recognition algorithms

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  1. Eye tracking to enhance facial recognition algorithms Balu Ramamurthy Brian Lewis December 15, 2011

  2. 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

  3. Contents • Biometrics Background • Eye Tracking Experiment • Facial Recognition Experiment • Facial Recognition Results • Conclusion • Future Work

  4. 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

  5. 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

  6. Verification Experiment • 2 Normalized faces shown to participant • Participant asked to say if same person or different person

  7. 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

  8. EyetrackingHeatmap

  9. Facial Recognition Procedure • Each correct image broken up in to 7 by 7 grid • Percentage of fixations for each block extracted.

  10. 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

  11. Facial Recognition Results

  12. 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

  13. 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

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