An eye localization, tracking and blink pattern recognition system
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
This study is to investigate the fundamental problems of, (1) facial feature detection and localization, especially eye features; and (2) eye dynamics, including tracking and blink detection. We first describe our contribution to eye localization. Following that, we discuss a simultaneous eye tracking and blink detection system. Facial feature detection is solved in a general object detection framework and its performance for eye localization is presented. A binary tree representation based on
... sentation based on feature dependency partitions the object feature space in a coarse to fine manner. In each compact feature subspace, independent component analysis (ICA) is used to get the independent sources, whose probability density functions (PDFs) are modeled by Gaussian mixtures. When applying this representation for the task of eye detection, a subwindow is used to scan the entire image and each obtained image patch is examined using Bayesian criteria to determine the presence of an eye subject. After the eyes are automatically located with binary tree-based probability learning, interactive particle filters are used for simultaneously tracking the eyes and detecting the blinks. The particle filters use classification-based observation models, in which the posterior probabilities are evaluated by logistic regressions in tensor subspaces. Extensive experiments are used to evaluate the performance from two aspects, (1) blink detection rate and the accuracy of blink duration in terms of the frame numbers; (2) eye tracking accuracy. We also present an experimental setup for obtaining the benchmark data in tracking accuracy evaluation. The experimental evaluation demonstrates the capability of this approach. ACM Reference Format: Wu, J. and Trivedi, M. M. 2010. An eye localization, tracking and blink pattern recognition system: Algorithm and evaluation.