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An eye localization, tracking and blink pattern recognition system: Algorithm and evaluation

Published:23 March 2010Publication History
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Abstract

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 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.

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  1. An eye localization, tracking and blink pattern recognition system: Algorithm and evaluation

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          Vishnu Vardhan Makkapati

          The identification of blink patterns can be applied in several cases, such as determining a subject's attentiveness, drowsiness, and consciousness. Several approaches have been proposed for this purpose. They involve three key steps: eye localization, tracking, and blink detection. Usually, eye tracking and blink detection are performed as two separate steps. This paper presents an approach that conducts these steps simultaneously. Eye detection is performed using a Bayesian criterion that relies on an accurate probability density estimator. A binary-tree-based statistical structure is used to solve this problem, where an exhaustive search is performed for eye image patches. Blink detection involves detecting the change in appearance between open and closed eyes. Two particle filters are used for this purpose. The filters track the eye location, as well as the scale of the eye image patches. The performance of the scheme is evaluated using several datasets. Eye localization is evaluated using face images from the FERET and Face Recognition Grand Challenge databases. The eye tracking and blink detection system is evaluated using videos taken with indoor and in-car cameras, and it is shown to be robust. Online Computing Reviews Service

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          • Published in

            cover image ACM Transactions on Multimedia Computing, Communications, and Applications
            ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 6, Issue 2
            March 2010
            119 pages
            ISSN:1551-6857
            EISSN:1551-6865
            DOI:10.1145/1671962
            Issue’s Table of Contents

            Copyright © 2010 ACM

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            New York, NY, United States

            Publication History

            • Published: 23 March 2010
            • Accepted: 1 December 2008
            • Revised: 1 September 2008
            • Received: 1 March 2008
            Published in tomm Volume 6, Issue 2

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