2013 | OriginalPaper | Chapter
Novel Adaptive Eye Detection and Tracking for Challenging Lighting Conditions
Authors : Mahdi Rezaei, Reinhard Klette
Published in: Computer Vision - ACCV 2012 Workshops
Publisher: Springer Berlin Heidelberg
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The paper develops a novel technique that significantly improves the performance of Haar-like feature-based object detectors in terms of speed, detection rate under difficult lighting conditions, and reduced number of false-positives. The method is implemented and validated for driver monitoring under very dark, very bright, and normal conditions. The framework includes a fast adaptive detector designed to cope with rapid lighting variations, as well as an implementation of a Kalman filter for reducing the search region and indirect support of eye monitoring and tracking. The proposed methodology effectively works under low-light conditions without using infrared illumination or any other extra lighting support. Experimental results, performance evaluation, and comparing a standard Haar-like detector with the proposed adaptive eye detector, show noticeable improvements.