Antonio Haro

Detecting and tracking eyes by using their physiological properties, dynamics, and appearance

Reliable detection and tracking of eyes is an important requirement for attentive user interfaces. In this paper, we present a methodology for detecting eyes robustly in indoor environments in real-time. We exploit the physiological properties and appearance of eyes as well as head/eye motion dynamics. Infrared lighting is used to capture the physiological properties of eyes, Kalman trackers are used to model eye/head dynamics, and a probabilistic based appearance model is used to represent eye appearance. By combining three separate modalities, with specific enhancements within each modality, our approach allows eyes to be treated as robust features that can be used for other higher-level processing.

Perceptual user interfaces using vision-based eye tracking

We present a multi-camera vision-based eye tracking method to robustly locate and track users eyes as they interact with an application. We propose enhancements to various vision-based eye-tracking approaches, which include (a) the use of multiple cameras to estimate head pose and increase coverage of the sensors and (b) the use of probabilistic measures incorporating Fishers linear discriminant to robustly track the eyes under varying lighting conditions in real-time. We present experiments and quantitative results to demonstrate the robustness of our eye tracking in two application prototypes.

A non-invasive computer vision system for reliable eye tracking

Knowing what the user is attending to and what they are looking at is essential for creating attentive user interfaces. Towards this end, we are building a reliable, real-time, non-invasive eye tracker using computer vision. Our system can robustly locate and track eyes without any calibration, and estimate the users focus of attention. We have built several higher-level processes on top of this tracking system and have done some user studies to test the viability of our approach.

Learning video processing by example

We present an algorithm that approximates the output of an arbitrary video processing algorithm based on a pair of input and output exemplars. Our algorithm relies on learning the mapping between the input and output exemplars to model the processing that has taken place. We approximate the processing by observing that pixel neighborhoods similar in appearance and motion to those in the exemplar input should result in neighborhoods similar to the exemplar output. Since there are not many pixel neighborhoods in the exemplars, we use techniques from texture synthesis to generalize the output of neighborhoods not observed in the exemplars. The same algorithm is used to learn such processing as motion blur color correction, and painting.