Eye detection under varying illumination using the retinex theory

Eye detection plays an important role in face recognition because eyes provide distinctive facial features. However, illumination effects such as heavy shadows and drastic lighting change make it difficult to detect eyes well in facial images. In this paper, we propose a novel framework for illumination invariant eye detection under varying lighting conditions. First, we use adaptive smoothing based on the retinex theory to remove the illumination effects. Second, we perform eye candidate detection using the edge histogram descriptor (EHD) on the illumination normalized-facial images. Third, we employ the support vector machine (SVM) classification for eye verification. Finally, we determine eye positions using eye probability map (EPM). Experimental results on the CMU-PIE, Yale B, and AR face databases demonstrate that the proposed method achieves high detection accuracy and fast computation speed in eye detection.

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