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Towards Integrating Statistical Color Features for Human Skin Detection

Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani

Abstract:

Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.

Keywords: Color space, neural network, random forest, skin detection, statistical feature.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1111592

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References:


[1] Kovac, J., P. Peer, and F. Solina, Human Skin Color Clustering for Face Detection, in EUROCON 2003. Computer as a Tool. The IEEE Region 8. 2003, IEEE. p. 144 - 148.
[2] Sobottka, K. and I. Pitas, A novel method for automatic face segmentation, facial feature extraction and tracking. Signal Processing: Image Communication, 1998. 12(3): p. 263-281.
[3] Zaidan, A.A., et al., An Automated Anti-Pornography System Using A Skin Detector Based on Artificial Intelligence: A Review. International Journal of Pattern Recognition and Artificial Intelligence, 2013. 27(04): p. 1350012.
[4] Abadpour, A. and S. Kasaei, Pixel-Based Skin Detection for Pornography Filtering. Iranian Journal of Electrical & Electronic Engineering, 2005. 1(3): p. 21-41.
[5] Elgammal, A., C. Muang, and D. Hu, Skin Detection - a Short Tutorial, in Encyclopedia of Biometrics, S. Li and A. Jain, Editors. 2009, Springer US. p. 1218-1224.
[6] Abdullah-Al-Wadud, M., S. Mohammad, and C. Oksam, A skin detection approach based on color distance map. EURASIP Journal on Advances in Signal Processing, 2009. 2008: p. 1-10.
[7] Kakumanu, P., S. Makrogiannis, and N. Bourbakis, A survey of skin-color modeling and detection methods. Pattern Recognition, 2007. 40(3): p. 1106-1122.
[8] Kawulok, M., J. Nalepa, and J. Kawulok, Skin Detection and Segmentation in Color Images, in Advances in Low-Level Color Image Processing, M.E. Celebi and B. Smolka, Editors. 2014, Springer Netherlands. p. 329-366.
[9] Gonzales, R. and E. Woods, Digital Image Processing. 2002, New Jersey: Prentice Hall.
[10] Chen, W., et al., Skin color modeling for face detection and segmentation: a review and a new approach. Multimedia Tools and Applications, 2014: p. 1-24.
[11] Bhoyar, K. and O. Kakde, Skin Color Detection Model Using Neural Networks and its Performance Evaluation 1. 2010.
[12] Al-Mohair, H.K., J. Mohamad-Saleh, and S.A. Suandi, Human Skin Color Detection: A Review on Neural Network Perspective. International Journal of Innovative Computing, Information and Control (ICIC), 2012. 8(12): p. 8115-8131.
[13] Abdullah-Al-Wadud, M. and C. Oksam. Skin Segmentation Using Color Distance Map and Water-Flow Property. in Information Assurance and Security, 2008. ISIAS '08. Fourth International Conference on. 2008.
[14] Al-Mohair, H.K., et al., Skin detection in luminace images using threshold technique. International Journal of Computer, the Internet and Management, 2007. 15(1): p. 25.
[15] Hasan, M.M. and P.K. Mishra, Superior Skin Color Model using Multiple of Gaussian Mixture Model. British Journal of Science, 2012. 6(1): p. 1-14.
[16] Phung, S.L., D. Chai, and A. Bouzerdoum. A universal and robust human skin color model using neural networks. in IJCNN'01. International Joint Conference on Neural Networks. Proceedings. 2001. IEEE.
[17] Lee, J.Y. and S.I. Yoo. An Elliptical Boundary Model for Skin Color Detection. in Proceeding of the International Conference on Imaging Science, Systems and Technology. 2002.
[18] Al-Mohair, H.K., J. Mohamad Saleh, and S.A. Suandi, Hybrid Human Skin Detection Using Neural Network and K-Means Clustering Technique. Applied Soft Computing, 2015. 33: p. 337-347.
[19] Doukim, C.A., et al., Combining neural networks for skin detection. arXiv preprint arXiv:1101.0384, 2011.
[20] Taqa, A.Y. and H.A. Jalab, Increasing the Reliability of Fuzzy Inference System-based Skin Detector. American Journal of Applied Science, 2010. 7(8): p. 1129-1139.
[21] Abadpour, A. and S. Kasaei. Comprehensive Evaluation of the Pixel-Based Skin Detection Approach for Pornography Filtering in the Internet Resources. in Int. Symposium on Telecommunications, Shiraz, Iran. 2005.
[22] Oghaz, M.M., et al., A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique. PloS one, 2015. 10(8): p. e0134828.