Fast eye detection using different color spaces

This paper presents a fast method for detecting the center of the eye in color face images using different color spaces. Specifically, this method consists of three stages. First, a color face image is transformed from the RGB color space to the YUV color space to extract the U color component image, whose binary image is utilized by projection functions to roughly locate the eye boundaries. Second, the center of the eye is identified within the eye boundaries through two different approaches: one approach converts the RGB image to a gray scale image and pinpoints the center of the eye with the lowest intensity value, while the other approach transforms the color image from the RGB color space to the HSV color space and singles out the center of the eye with the largest intensity variation compared with its 8-neighbors in the H color component image. Note that the better result due to these two approaches is chosen as the center of the eye. Finally, the center of the eye is adjusted based on the prior knowledge of anthropometry for further improving the accuracy of eye detection. Experiments using 974 randomly chosen Face Recognition Grand Challenge (FRGC) images show the feasibility of our eye detection method. In particular, the eye detection rate of both eye centers being accurately detected is 95.4%.