Eye Detection and Eye Blink Detection Using AdaBoost Learning and Grouping

This paper proposes a precise eye detection and eye blink detection algorithm. Eye detection combines and separates scanning results based on an MCT-based AdaBoost detector. The algorithm detects eyes by applying the eye detector to eye candidate regions of a face. To eliminate outliers, we select an eye candidate group by grouping eye candidates. A refinement process using the average position of eye candidates in the selected eye candidate group obtains reliable detection results. Eye blink detection uses an MCT-based AdaBoost classifier, which discriminates between opened and closed eyes. The eye detection rate is 99.34% at the 0.1 normalized error on the BioID database. The eye blink detection accuracy is 96% at the 0.03 FAR on our blink database, which contains 400 images. The average processing time is 1 ms and 30 ms in a PC (Core2Duo 3.2GHz) and smart phone (PXA312), respectively.