Skin Color Segmentation Using Coarse-to-Fine Region on Normalized RGB Chromaticity Diagram for Face Detection

This paper describes a new color segmentation based on a normalized RGB chromaticity diagram for face detection. Face skin is extracted from color images using a coarse skin region with fixed boundaries followed by a fine skin region with variable boundaries. Two newly developed histograms that have prominent peaks of skin color and non-skin colors are employed to adjust the boundaries of the skin region. The proposed approach does not need a skin color model, which depends on a specific camera parameter and is usually limited to a particular environment condition, and no sample images are required. The experimental results using color face images of various races under varying lighting conditions and complex backgrounds, obtained from four different resources on the Internet, show a high detection rate of 87%. The results of the detection rate and computation time are comparable to the well known real-time face detection method proposed by Viola-Jones [11],[12].

[1]  Xin-He Xu,et al.  Face detection based on skin color , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[2]  A. Pacut,et al.  Particle filters for multi-face detection and tracking with automatic clustering , 2007, 2007 IEEE International Workshop on Imaging Systems and Techniques.

[3]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[4]  Yi-Ting Huang,et al.  A novel method for detecting lips, eyes and faces in real time , 2003, Real Time Imaging.

[5]  Cai Anni,et al.  Automatic face segmentation in YCrCb images , 1999, Fifth Asia-Pacific Conference on ... and Fourth Optoelectronics and Communications Conference on Communications,.

[6]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Koichi Yamada,et al.  A New Approach on Red Color Thresholding for Traffic Sign Recognition System , 2007 .

[8]  Mika Laaksonen,et al.  Using the skin locus to cope with chang-ing illumination conditions in color-based face tracking , 2000 .

[9]  Matti Pietikäinen,et al.  Color-based face detection using skin locus model and hierarchical filtering , 2002, Object recognition supported by user interaction for service robots.

[10]  Rainer Lienhart,et al.  Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection , 2003, DAGM-Symposium.

[11]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  E. Granum,et al.  Skin colour detection under changing lighting conditions , 1999 .

[13]  Shigeru Akamatsu,et al.  Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[14]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[15]  Richard B. Reilly,et al.  VALID: A New Practical Audio-Visual Database, and Comparative Results , 2005, AVBPA.

[16]  Jacek Czyz.  Object Detection in Video via Particle Filters , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[17]  Charles A. Bouman,et al.  A simple and efficient face detection algorithm for video database applications , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[18]  P. Campadelli,et al.  A COLOR-BASED METHOD FOR FACE DETECTION , 2003 .

[19]  Sebastian Lang,et al.  Improving adaptive skin color segmentation by incorporating results from face detection , 2002, Proceedings. 11th IEEE International Workshop on Robot and Human Interactive Communication.

[20]  Mika Laaksonen,et al.  Skin detection in video under changing illumination conditions , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.