Comparison of Eyes Characteristics in Different Color Spaces

Eye characteristics are significant information in pattern recognition. They are widely applied in many fields, such as human-computer interaction, face recognition and 3D face modeling from 2D images. Generally, the eye information detection is Based on gray images. However, color images provide more information than gray images. According to different applications, choosing a suitable color space is very important. In this paper, several eye features are compared from the view point of different color spaces, such as RGB, HIS, HSV. And some experiments have been done in the three color spaces. The extraordinary bright area of pupil in the H channel of HSV color space is confirmed. Moreover, pupil center is detected and estimated using morphology processing method in H channel. The results show that eyes features are more obvious in the HSV space, and feature extraction is more convenient. Furthermore, the experimental analysis can provide reference for future research on eye characteristic recognition.

[1]  Joan Serra,et al.  Image segmentation , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[2]  Mohamed Rizon,et al.  Detection of eyes from human faces by Hough transform and separability filter , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[3]  Feipei Lai,et al.  Region-based template deformation and masking for eye-feature extraction and description , 1997, Pattern Recognit..

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

[5]  Jianxin Wu,et al.  Efficient face candidates selector for face detection , 2003, Pattern Recognit..

[6]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Zhu Shan-an Hough transform for eye feature extraction , 2008 .

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

[9]  Frank Y. Shih,et al.  Image Segmentation , 2007, Encyclopedia of Biometrics.

[10]  Alan L. Yuille,et al.  Feature extraction from faces using deformable templates , 2004, International Journal of Computer Vision.

[11]  Sang Uk Lee,et al.  Color image segmentation based on 3-D clustering: morphological approach , 1998, Pattern Recognit..