| Paper title: |
Real Time Face Detection Using Skin Detection (Block Approach) |
| Published in: | Issue 1, (Vol. 5) / 2011 |
| Publishing date: | 2010-04-29 |
| Pages: | 75-81 |
| Author(s): | SHARIF Muhammad, MOHSIN Sajjad, JAVED Muhammad Younas |
| Abstract. | Role of Face recognition in security applications can never be overstated, that is why researchers around the world are still actively participating in this area. The initial step in the procedure of face recognition is to detect it efficiently. In this paper, a hybrid technique is being proposed using skin detection (in RGB color space) and block approach. This fusion is proposed to achieve fast skin detection. Along with the fusion, template matching is also used for detection purpose. Block approach for skin detection means dividing the image into square blocks virtually and then applying the detection ratios on corner pixels. If the corner pixels satisfy the ratios completely, the whole block will be considered as a skin block and if none of them satisfies these ratios, the block will be treated as a non-skin block. Even if some of the corner pixels satisfy the ratios, then the block will be searched for skin pixels. In this way, it will rapidly detect a face in an image including high resolution pixels. The experiments are carried out and the results have shown the significant improvements both in time and accuracy of the detection, that has prove the fastness and robustness of the proposed technique. |
| Keywords: | Face Recognition, Real Time, Skin Detection, Block Approach |
| References: | 1. Guo, S. Li, Z., and Chan, K. “Face Recognition by Support Vector Machines”. Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France.pp. 196-201, March 2000. 2. Othman and Aboulnasr, T. Hybrid Hidden Markov Model for Face Recognition. Proc. of the Fourth IEEE Southwest Symposium on Image Analysis and Interpretation, Austin, Texas, pp.36-40, April 2000. 3. Xi, I. Podolak, T., and Lee, S. Facial Component Extraction and Face Recognition with Support Vector Machine. Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition, Washington D. C, pp.83-88, May 2002. 4. M. Yang, D. J. Kriegman and N. Ahuja. Detecting faces in images: A survey. IEEE Trans. Pattern Analysis and Machine Intell., vol. 24, pp. 34-58, 2002. 5. W. H. Lai and C. T. Li. Skin Color based Face Detection in Color Images. In Proceeding of the IEEE Int’l Conference on Video and Signal Based Surveillance, pp. 56, 2006. 6. K. W. Wong, K-M. Lam and W-C. Sui. A robust scheme for live detection of human faces in color images. Signal Processing: Image Communication, vol 18, pp. 103-114, 2003. 7. H. Ishii, M. Fukumiand N. Akamatsu. Face detection based on skin color information in visual scenes by neural networks. Int’l Conference on Sys., Man and Cyb, pp. 350-355, 1999. 8. Rob Byrd, RanjaniBalaji, “Real Time 2-D Face Detection Using Color Ratios and KMean Clustering”, ACM Southeast Regional Conference, Proceedings of the 44th annual Southeast regional conference, pp. 644-648, Melbourne, Florida 2006 9. Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Mille “Labeled Faces in the Wild”. http://viswww.cs.umass.edu/lfw/ 10. B. Takacs and H. Wechsler, “Fast searching of digital face libraries using binary image metrics”, IEEE Int. Conf. on Patt. Recog., pp. 1235-1237, August 1998. 11. Xiao-Wei Wang, Zhong Wang, Jun-Tao Sun, Hui-Min Zhang, “The Correlation Template matching algorithm based td filter and eso filter” , Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, pp. 5361 – 5365, August 2005 12. Henry Chang, Ulises Robles, “Face Detection “,http://wwwcsstudents.stanford.edu/~robles/ee368/main.html, 2000 13. D. G. Sim, O. K. Kwon and R. H. Park, “Object matching algorithms using robust hausdorff distance measures”, IEEE Trans. on Image Processing, Vol. 8, pp. 425-429, 1999. |
| Back to the journal content | |
