Comparative Study on Color Components for PCA-Based Face Recognition

〈Summary〉 Using color information can significantly improve the face recognition rate instead of using the grayscale luminance image. However, there are few works that try to compare the color space models on face recognition. In this paper, we investigate thirty different color space models on face recognition using the classical principal component analysis (PCA). Through the extensive experiments we find that after successfully diminishing the influence of the illumination the recognition accuracy can be improved by 4.6∼5.5 percent points.

[1]  David A. Forsyth,et al.  Finding Naked People , 1996, ECCV.

[2]  Vijayan K. Asari,et al.  An improved face recognition technique based on modular PCA approach , 2004, Pattern Recognit. Lett..

[3]  R. V. Rossel,et al.  Colour space models for soil science , 2006 .

[4]  Luis Torres,et al.  Automatic face recognition for video indexing applications , 2002, Pattern Recognit..

[5]  Erik Reinhard,et al.  Color Transfer between Images , 2001, IEEE Computer Graphics and Applications.

[6]  T. Kanade,et al.  Color information for region segmentation , 1980 .

[7]  Changjun Li,et al.  The CIECAM02 Color Appearance Model , 2002, CIC.

[8]  Omachi Shinichiro,et al.  Comparative Study on Different Color Space Models for Skin Color Segmentation , 2006 .

[9]  Georgios Tziritas,et al.  Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis , 1999, IEEE Trans. Multim..

[10]  Luis Torres,et al.  The importance of the color information in face recognition , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[11]  Shih-Fu Chang,et al.  A highly efficient system for automatic face region detection in MPEG video , 1997, IEEE Trans. Circuits Syst. Video Technol..

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

[13]  Qian Chen,et al.  Face detection by fuzzy pattern matching , 1995, Proceedings of IEEE International Conference on Computer Vision.

[14]  Narendra Ahuja,et al.  Gaussian mixture model for human skin color and its applications in image and video databases , 1998, Electronic Imaging.

[15]  Bruce A. Draper,et al.  Analyzing PCA-based Face Recognition Algorithms: Eigenvector Selection and Distance Measures , 2003 .

[16]  Shinichiro Omachi,et al.  Comparative Study on Different Color Space Models for Skin Color Segmentation , 2010 .

[17]  J. Birgitta Martinkauppi,et al.  Behavior of skin color under varying illumination seen by different cameras at different color spaces , 2001, IS&T/SPIE Electronic Imaging.

[18]  Mark D. Fairchild,et al.  Development and Testing of a Color Space (IPT) with Improved Hue Uniformity , 1998, CIC.

[19]  Nathan Moroney,et al.  A Radial Sampling of the OSA Uniform Color Scales , 2003, Color Imaging Conference.

[20]  Jing Li Wang,et al.  Color image segmentation: advances and prospects , 2001, Pattern Recognit..