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1.11.2014, - 5.12.2014

- , 414056, ; ; . . ; ї; evgeny.erman@gmail.com.
, 414056; ; ; ї; doha_gomaa@yahoo.com.
E. A. Erman, Mamdouh Mokhammed Gomaa Mokhammed
FACE DETECTION IN IMAGE BY USING A COMBINATION OF VIOLA - JONES METHOD AND SKIN DETECTION ALGORITHMS
Abstract. In the past few years, there have been proposed many algorithms for face detection, using different approaches. Algorithms for detecting faces are used in the vision systems, robotics, video surveillance and access control systems. The problem of face detection has serious practical perspective and interest for a great research. Often, it is the "first step" in the process of solving the problems of a higher level (for example, recognition of faces, facial expression recognition). For the successful system, face detection algorithms are necessary to provide high-speed operation and minimal number of false detection. The method of Viola - Jones is one of the best indicators for the effectiveness of recognition ratio/performance. However, this method in many cases gives a large number of false detection. The color of human skin is one of the features that helps make detection of face. This paper solved the problem of detection of face area by using the proposed method, which based on the combination of Viola & Jones method with skin detection method by using Log opponent and YIQ color spaces. To identify faces in the images, it is proposed to use, firstly, the Viola - Jones method for the original image. Then for each output region, the skin detection method is used to classify the evaluated area of the human skin. The experimental results show that the proposed method detected 95.75 % of the faces presented on a set of test images, and substantially reducing the probability of the false detection.
Key words: face detection, skin detection, Viola - Jones method, Log opponent color space, YIQ color space.
REFERENCES
1. Viola P., Jones M. J. Robust real_time face detection. International Journal of Computer Vision, 2004, vol. 57, no. 2, pp. 137-154.
2. Bradski G., Kaebler A. Learning OpenCV. Published by O'Reilly Media, 2008. P. 495-512.
3. Gonsales P., Vuds R. Tsifrovaia obrabotka izobrazhenii [Digital processing of the image]. Moscow, Tekhnosfera Publ., 2005. P. 1072.
4. Zahra S. T., Rahmat R. W., Udzir N. B., Kheirkhah E. A. Hybrid Face Detection System using combination of Appearance-based and Feature-based methods. International Journal of Computer Science and Network Security, 2009, vol. 9, no. 5, pp. 181-185.
5. Gururaj P., Dayanand J., M. Dhananjay. An Analysis of Skin Pixel Detection using Different Skin Color Extraction Techniques. J. International Journal of Computer Applications, 2012, vol. 54, no. 17, pp. 1-5.
6. Duan L., Cui G., Gao W., Zhang H. Adult image detection method base-on skin color model and support vector machine. In Asian Conference on computer Vision. Melbourne, Australia, 2002. P. 797-800.
7. Tarek Abd El-Hafeez. A new system for extracting and detecting skin color regions from pdf documents. International Journal on Computer Science and Engineering, 2010, vol. 2 (9), pp. 2838-2846.
8. Fleck M., Forsyth D., Bregler C. Finding Naked People. In Proc. of the ECCV, 1996, vol. 2, pp. 592-602.
9. BaoDataBase. Available at: http://www.facedetection.com/facedetection/datasets.htm.
10. Omaima N. A. Review of face detection systems based artificial neural networks algorithms. International Journal of Multimedia & Its Applications, 2014, vol. 6, pp. 448-455.
The article submitted to the editors 1.11.2014, in the final version - 5.12.2014
INFORMATION ABOUT THE AUTHORS
Erman Evgeniy Anatolievich Russia, 414056, Astrakhan; Astrakhan State University; Candidate of Technical Sciences; Assistant Professor of the Department "Information Technologies and Security"; evgeny.erman@gmail.com.
Mamdouh Mokhammed Gomaa Mokhammed Russia, 414056, Astrakhan; Astrakhan State University; Postgraduate Student of the Department "Information Technologies and Security"; d oha_gomaa @yahoo.c om.