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Fast Face Detection in Video Using The HCr and Adaptive Thresholding Method  

신승주 (세종대학교 전자공학과)
최석림 (세종대학교 전자공학과)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.41, no.6, 2004 , pp. 61-71 More about this Journal
Abstract
Recently, various techniques for face detection are studied, but most of them still have problems on processing in real-time. Therefore, in this paper, we propose novel techniques for real-time detection of human faces in sequential images using motion and chroma information. First, background model is used to find a moving area. In this procmoving area. edure, intensity values for reference images are averaged, then skin-color are detected in We use HCr color-space model and adaptive threshold method for detection. Second, binary image labeling is applied to acquire candidate regions for faces. Candidates for mouth and eyes on a face are obtained using differences between green(G) and blue(B), intensity(I) and chroma-red(Cr) value. We also considered distances between eye points and mouth on a face. Experimental results show effectiveness of real-time detection for human faces in sequential images.
Keywords
Face Detection; Color Space; HCr; Adative Threshold;
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1 K. Sobottka, I. Pitas, 'Extraction of Facial Regions and Features Using Color and Shape Information', IEEE International Conference on Pattern Recognition Vol. 3, August 1996   DOI
2 D. Chai, K.N. Ngan, 'Face Segmentation Using Skin-Color Map in Videophone Applications', IEEE Transactions on Circuits and Systems for Video Technology, Vol. 9, No.4, June 1999   DOI   ScienceOn
3 Rein-Lien Hsu, Mohamed Abdel-Mottaleb, 'Face Detection in Color Images', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp.696-706, May 2002   DOI   ScienceOn
4 Jeonghee Park, Jung Seo, Dongun An and Seongjong Chung, 'Detection of Human Faces using Skin Color and Eyes', in Proceeding of IEEE International Conference on Multimedia and Expo, vol.1, pp.133-136, 2000   DOI
5 J. Yang, A. Waibel, 'A Real-Time Face Tracker', IEEE Workshop on Applications of Computer Vision, Sarasota, Florida, 1996   DOI
6 C. Garcia, G. Tzirtas, 'Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis', IEEE Transactions on Multimedia, Vol.1, No.3, September 1999   DOI
7 M.J. Jones and James M.Rehg, 'Statistical Color Models with Application to Skin Detection', Cambridge Research Laboratory Technical Report Series, 1998
8 L. Jorado, M. Perrone, J. P. Costeria, 'Active Face and Feature Tracking', in Processings of the 10th International Conference on Image Analysis and Processing, Kobe, Japen, Sep. 1999   DOI
9 Henry A. Rowley, Shumeet Baluja, Takeo Kanade,'Neural Network-Based Face Detection', IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No.1, January 1998   DOI   ScienceOn
10 Raphael Feraud, Olivier J. Bernier, Jean-Emmanuel Viallet, Michel Collobert, 'A Fast and Accurate Face Detector Based on Neural Networks', IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 1, January 2001   DOI   ScienceOn
11 E. Osuna, R.Freund, F. Girosi, 'Training support vector machines: An Application to face detection', IEEE Proc. of Int. Conf. On Computer Vision and Pattern Recognition, June, 1997   DOI
12 M.-H. Yang, D. J. Kriegman, and N. Ahuja, 'Detecting faces in images: A survey,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 34-58, Jan. 2002   DOI   ScienceOn
13 K. Sung, T. Poggio 'Example-Based Learning for View-Based Human Face Detection', IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20 No:1, pp. 39-51, Jan.1998   DOI   ScienceOn