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A Robust Eye Detection Approach Based on Edge-related Information


Qiu Chen, Koji Kotani, Feifei Lee, Tadahiro Ohmi


Vol. 9  No. 9  pp. 22-27


In this paper, we propose an improved algorithm for robust eye detection. First, instead of histogram back-projection method, AdaBoost method is utilized to extract the rough face region, and then iris candidates are detected by using rectangular separability filter. By calculating the similarities of pairs of iris candidates, we determine the pair of iris, which has the largest similarity among others. The similarity of a pair of iris candidates consists of the separability of the pair of iris candidates, the VQ histogram similarity and normalized correlation coefficient between the region including the pair of iris candidates and eye template. Experimental results show the iris detection rate of the proposed algorithm of 96.7% for 516 images of 86 persons without spectacles in the AR database.


Eye detection, AdaBoost, Rectangular separability filter, Vector quantization (VQ)