Eye tracking to enhance facial recognition algorithms

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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

Used 10 males and 10 females

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

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

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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