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Face Detection from ATIS Spiking Output

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Face Detection Task: Is there any part of a face present or not? (not trying to localize face or identify person) NO YES NO YES

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ATIS data collection Training data: 4 subjects (static cam, unoccluded face) Test data (face present) Hand-drawn cartoon faces (slightly-moving cam, unoccluded face) Slightly-moving cam, unoccluded face Moving cam, face occluded sometimes Test data (face absent) 6 hand gestures Person walking along school corridor Dot pattern on kitchen whiteboard Pool game

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Training / testing procedure Chop all data (training and testing) up into short segments of 5000 spikes Primarily for practical reasons and convenience Train on 4 subjects data separately Static camera, subjects head is rotating/moving Test on various data Vary detection threshold to produce ROC curve (True Positive rate vs. False Positive rate) Primary metric is AUC (area under ROC curve)

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Face detection algorithm (briefly) Use Garricks HFirst algorithm as starting point S1 layer: oriented Gabor filters over spike input C1 layer: local position invariance Training: Run training images (all containing faces) up to C1 Extract face templates (regions of C1 spike patterns) Testing: Run test image up to C1 Match C1 spike patterns to templates If # matches exceeds threshold, then face is present

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Results (1 of 4) Training: 4 subjects (static cam, unoccluded face) Testing: cross-testing on the other 3 subjects (static cam, unoccluded face) Face-absent data: hand gestures, pool game, corridor, etc. Objective: test generalization to untrained subjects Sample training segment Sample test segment Sample test segment (face absent)

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Results (1 of 4) Training: 4 subjects (static cam, unoccluded face) Testing: cross-testing on the other 3 subjects (static cam, unoccluded face) Face-absent data: hand gestures, pool game, corridor, etc. Objective: test generalization to untrained subjects Results: mean AUC = 0.863 Trained on Subject C.T. Trained on Subject G.O. Trained on Subject H.A. Trained on Subject M.M.

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Results (2 of 4) Training: 4 subjects (static cam, unoccluded face) Testing: hand-drawn cartoon faces (slightly-moving cam, unoccluded face) Face-absent data: same as previous Objective: test generalization to artificial faces Sample training segment Sample test segment Sample test segment (face absent)

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Results (2 of 4) Training: 4 subjects (static cam, unoccluded face) Testing: hand-drawn cartoon faces (slightly-moving cam, unoccluded face) Face-absent data: same as previous Objective: test generalization to artificial faces Results: mean AUC = 0.740 Trained on Subject C.T. Trained on Subject G.O. Trained on Subject H.A. Trained on Subject M.M.

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Results (3 of 4) Training: 4 subjects (static cam, unoccluded face) Testing: untrained subjects (slightly-moving cam, unoccluded face) Face-absent data: same as previous Objective: test generalization to moving cam Sample training segment Sample test segment Sample test segment (face absent)

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Results (3 of 4) Training: 4 subjects (static cam, unoccluded face) Testing: untrained subjects (slightly-moving cam, unoccluded face) Face-absent data: same as previous Objective: test generalization to moving cam Results: mean AUC = 0.955 Trained on Subject C.T. Trained on Subject G.O. Trained on Subject H.A. Trained on Subject M.M. (Not sure why AUC is so good!)

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Results (4 of 4) Training: 4 subjects (static cam, unoccluded face) Testing: untrained subjects (slightly-moving cam, faces occluded sometimes) Face-absent data: same as previous Objective: test generalization to moving cam, occlusions Sample training segment Sample test segment Sample test segment (face absent)

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Results (4 of 4) Training: 4 subjects (static cam, unoccluded face) Testing: untrained subjects (slightly-moving cam, faces occluded sometimes) Face-absent data: same as previous Objective: test generalization to moving cam, occlusions Results: mean AUC = 0.883 Trained on Subject C.T. Trained on Subject G.O. Trained on Subject H.A. Trained on Subject M.M.

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Limitations Dataset is small and probably not too difficult Accuracy Nowhere close to state-of-the-art for face detection in grayscale images The real region of interest is FP rate