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 = {a1, a2, … an} where ai is a

rectangle in Y H = {H1, H2, … Hn} is a random vector

assigning each patch Hi 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 | Hi = 1)p(y | hiHi = 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