Automatic eye detection using intensity and edge information

We propose a new algorithm to detect the pupils of both eyes from a human face in an intensity image. First, feature points which are the candidates for the pupils of both eyes are extracted from the face image by using the feature template proposed by Lin and Wu (see IEEE Trans. Image Processing, vol.8, no.6, p.834-45 (1999). Next, the proposed algorithm computes a cost for each pair of feature points satisfying a spatial constraint. The cost is computed by searching for a circular region corresponding to the iris around each feature point. Finally, the algorithm determines a pair of feature points with the smallest cost to be the pupils of both eyes. As a result of the experiment using all faces without spectacles in the face database of the University of Bern, the success rate of the proposed algorithm was 93.0% on the average. Also, if looking-down faces are excluded, the success rate of the proposed algorithm was 97.1% on the average.

[1]  Xiaobo Li,et al.  Towards a system for automatic facial feature detection , 1993, Pattern Recognit..

[2]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Stanley R Sternberg,et al.  Grayscale morphology , 1986 .

[4]  Osamu Yamaguchi,et al.  Facial Feature Point Extraction Method Based on Combination of Shape Extraction and Pattern Matching , 1998 .

[5]  Rae-Hong Park,et al.  Recognition of human front faces using knowledge-based feature extraction and neurofuzzy algorithm , 1996, Pattern Recognit..

[6]  Hong Yan,et al.  Locating and extracting the eye in human face images , 1996, Pattern Recognit..

[7]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[8]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Mohamed Rizon,et al.  Robust extraction of eyes from face , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[10]  Jack Sklansky,et al.  Finding circles by an array of accumulators , 1975, Commun. ACM.

[11]  Ja-Ling Wu,et al.  Automatic facial feature extraction by genetic algorithms , 1999, IEEE Trans. Image Process..

[12]  David Beymer,et al.  Face recognition under varying pose , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.