Computer Vision: Eye Tracking By: Geraud Campion Michael O’Connor

Frame Work A brief overview of eye tracking, formulas for image dissection, and some current applications of eye tracking.

Eye Tracking Why Eyes?  Failures of facial recognition due to poor alignment  Eye and eye movement are important to human interaction

Eye Tracking Current Approaches  Visible Spectrum Cameras  Near-Infra-Red cameras (NIRs) Work well in optimal conditions: fast and accurate Not so good otherwise: a lot of false positives Not a great help in the field of psychology or neurology

Another Technique Reflected Light from the Eye:

Reflected Light

Eye Tracking Search Methods  Probabilistic Methods Bayesian Inference Model Key to this is that an image is cut into a collage of rectangles of arbitrary size

Eye Tracking Y is a random matrix y is a specific point A = {a 1, a 2, … a n } where a i is a rectangle in Y H = {H 1, H 2, … H n } is a random vector assigning each patch H i a value: 1 object of interest, -1 background, 0 not rendered

Eye Tracking All this leads to this formula P(H = 1 | y) = Σ [P(H=1) p(h | H i = 1)p(y | h i H i = 1) ]/ p(y) which is the probability that a portion y holds our object of interest:

Eye Tracking Situation Based Reference  Make a hierarchy of “context dependent experts”  Each expert uses probabilistic methods  Then we use this formula: p(o|y) = ∫p(s|y) p(o|sy) dh Y – an observed image S – contextual situation O – location of left eye of the face on image

Applications Camera Mouse  Eyebrow/blink patterns for clicking Driver Fatigue Detection  (750 deaths, 20,000 injuries / yr from commercial vehicles) Detecting Amblyopia in Children Toys

Some Results One Person Two People Eyebrow Clicker VTOY