Automated switching system for skin pixel segmentation in varied lighting

In Computer Vision, colour-based spatial techniques often assume a static skin colour model. However, skin colour perceived by a camera can change when lighting changes. In common real environment multiple light sources impinge on the skin. Moreover, detection techniques may vary when the image under study is taken under different lighting condition than the one that was earlier under consideration. Therefore, for robust skin pixel detection, a dynamic skin colour model that can cope with the changes must be employed. This paper shows that skin pixel detection in a digital colour image can be significantly improved by employing automated colour space switching methods. In the root of the switching technique which is employed in this study, lies the statistical mean of value of the skin pixels in the image which in turn has been derived from the Value, measures as a third component of the HSV. The study is based on experimentations on a set of images where capture time conditions varying from highly illuminated to almost dark.

[1]  Shaogang Gong,et al.  Tracking colour objects using adaptive mixture models , 1999, Image Vis. Comput..

[2]  James M. Rehg,et al.  Statistical Color Models with Application to Skin Detection , 2004, International Journal of Computer Vision.

[3]  Yang Jing-yu,et al.  Rotation-invariant Face Detection in Color Images with Complex Background , 2008 .

[4]  Stephen J. McKenna,et al.  A comparison of skin history and trajectory-based representation schemes for the recognition of user-specified gestures , 2004, Pattern Recognit..

[5]  Chen Mao-yuan.  Design of Color Image Skin Area Segmentation System in the Matlab Envionment , 2007 .

[6]  Anil K. Jain,et al.  Face Detection in Color Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Abdesselam Bouzerdoum,et al.  Skin segmentation using color pixel classification: analysis and comparison , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Ankit Chaudhary,et al.  A Vision based Real Time System to Control Remote Robotic Hand Fingers , 2011 .

[9]  Ankit Chaudhary,et al.  Fingertip Detection: A Fast Method with Natural Hand , 2012, ArXiv.

[10]  A. Bouzerdoum,et al.  A Bayesian approach to skin color classification in YCbCr color space , 2000, 2000 TENCON Proceedings. Intelligent Systems and Technologies for the New Millennium (Cat. No.00CH37119).

[11]  Chuan-Yu Chang,et al.  Adaptive Color Space Switching Based Approach for Face Tracking , 2006, ICONIP.

[12]  Cuixiang Liu,et al.  A Robust Method for Skin Detection and Segmentation of Human Face , 2009, 2009 Second International Conference on Intelligent Networks and Intelligent Systems.

[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]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[15]  Shah Mostafa Khaled,et al.  Comparative Study of Statistical Skin Detection Algorithms for Sub-Continental Human Images , 2010, ArXiv.

[16]  S. S. Vinsley,et al.  Skin Detection Using Color Pixel Classification with Application to Face Detection: A Comparative Study , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[17]  Qing Chen,et al.  Hand Gesture Recognition Using Haar-Like Features and a Stochastic Context-Free Grammar , 2008, IEEE Transactions on Instrumentation and Measurement.

[18]  Ankit Chaudhary,et al.  ABHIVYAKTI: A Vision Based Intelligent System for Elder and Sick Persons , 2011, ArXiv.

[19]  Jason Brand,et al.  A comparative assessment of three approaches to pixel-level human skin-detection , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

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

[21]  Xiang Xiao-yan.  Face Detection Based on Skin Segmentation and Features Location , 2008 .

[22]  Stewart A. Rounds.  DEVELOPMENT OF A NEURAL NETWORK MODEL FOR DISSOLVED OXYGEN IN THE TUALATIN RIVER, OREGON , 2002 .

[23]  Francis Quek,et al.  Comparison of five color models in skin pixel classification , 1999, Proceedings International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems. In Conjunction with ICCV'99 (Cat. No.PR00378).

[24]  Joel R. Jackson,et al.  Color image segmentation in RGB using vector angle and absolute difference measures , 2006, 2006 14th European Signal Processing Conference.

[25]  Ankit Chaudhary,et al.  ABHIVYAKTI: Hand Gesture recognition using Orientation histogram in different light conditions , 2011, IICAI.