Generalized Gaussian Density for Skin Detection in DCT Domain

• Autorzy: Ghouzali S. , Hemami S. , Rziza M. , Aboutajdine D.
• Języki publikacji: EN

Abstrakty

EN

In this paper, we propose a highly efficient algorithm to model the human skin color. The algorithm involves generating a discrete Cosine transform (DCT) at each pixel location, using the surrounding points. The DCT coefficients incorporate the pixel color and texture information to distinguish between skin and non-skin. A generalized Gaussian distribution (GGD) is used in this framework to model the DCT coefficients at low frequencies. Next, the model parameters are estimated using the maximum-likelihood (ML) criterion applied to a set of training skin samples. Finally, each pixel is classified as skin if its likelihood ratio exceeds some threshold. The experimental results show that our model avoids excessive false detection while still retaining a high degree of correct detection.

Słowa kluczowe

EN

skin detection; generalized Gaussian distribution; discrete cosine transform; image segmentation

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2009
• Tom: Vol. 18, No. 2
• Strony: 201--213
• Opis fizyczny: Bibliogr. 23 poz., il., wykr.

Twórcy

autor

Ghouzali S.

autor

Hemami S.

autor

Rziza M.

autor

Aboutajdine D.

• School of Electrical and Computer Engineering, Cornell University, Ithaca, New York 14853, USA

Bibliografia

• [1] Ahmed N., Natarajan T., Rao K. R.: Discrete Cosine Transform, IEEE Transactions Computers, 23, 90-94, 1974.
• [2] Clarke R. J.: Transform Coding of Images. Academic Press, New York, NY. 1985.
• [3] Varanasi M. K., Aazhang B.: Parametric generalized Gaussian density estimation, The Journal of the Acoustical Society of America, 86(4), 1404-1415, 1989.
• [4] Rao K., Yip P.: Discrete Cosine Transform Algorithms, Advantages, Applications. Academic Press, New York, NY. 1990.
• [5] Müller F.: Distribution shape of two-dimensional DCT coefficients of natural images, Electron. Lett., 29(22), 1935-1936, 1993.
• [6] Yang G., Waibel A.: A Real-time Face Tracker, Workshop on Applications of Computer Vision, 142-147, 1996.
• [7] Bae H.-J., Jung S.-H.: Image Retrieval Using Texture Based on DCT, Int'l Conference on Information, Communications and Signal Processing, 1065-1068, 1997.
• [8] Yang M.-H., Ahuja N.: Detecting Human Faces in Color Images, International Conference on Image Processing (ICIP'98), 1, 127-130, 1998.
• [9] Zarit B. D., Super B. J., Quek F. K. H.: Comparison of Five Color Models in Skin Pixel Classification, ICCV'99 Int'l Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 58-63, 1999.
• [10] Yang M., Ahuja N.: Gaussian Mixture Model for Human Skin Color and Its Application in Image and Video Databases, Proc. of the SPIE, Conf. on Storage and Retrieval for Image and Video Databases, 458-466, 1999.
• [11] Terrillon J. C., Shirazi M. N., Fukamachi H., Akamatsu S.: Comparative Performance of Different Chrominance Models and Chrominance Spaces for the Automatic Detection of Human Faces in Color Images, IEEE. Int'l Conf. on Face and Gesture Recognition (FG 2000), 54-61, 2000.
• [12] Lin C., Fan K. C.: A Color-Triangle-Based Approach to the Detection of Human face, British Machine Vision Conference (BMVC'00), 1811, 359-368, 2000.
• [13] Duda R. O., Hart P. E., Stork D. G.: Pattern Classification, John Wiley and Sons. 2001.
• [14] Hsu R.-L., Abdel-Mottaleb M., Jain A. K.: Face Detection in Color Images, IEEE Trans. Pattern Analysis and Machine Intelligence, 24(5), 696-706, 2002.
• [15] Caetano T. S., Olabarriaga S. D., Barone D. A. C.: Performance evaluation of single and multiple-Gaussian models for skin color modeling, Proceedings XV Brazilian Symposium on Computer Graphics and Image Processing, 275-282, 2002.
• [16] Jones M. J., Rehg J. M.: Statistical color models with application to skin detection, Int'l Journal of Computer Vision, 46(1), 81-96, 2002.
• [17] Wei J.: Image segmentation based on situational DCT descriptors, Pattern Recogn. Lett., 23(1-3), 295-302, 2002.
• [18] Do M. N., Vetterli M.: Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance, IEEE Trans. Image Processing, 11, 146-158, 2002.
• [19] Vezhenevets V., Sazonov V. and Andreeva A.: A survey on Pixel-Based skin color detection techniques, Proceedings of Graphicon, 85-92, 2003.
• [20] Zhu Q., Wu C.-T., Cheng K.-T., Wu Y.-L.: An adaptive skin model and its application to objectionable image filtering, Proceedings of the 12th annual ACM Int'l Conf. on Multimedia, 56-63, 2004.
• [21] Ruiz-del-Solar J. and Verschae R. : Skin Detection using Neighborhood Information, IEEE Int'l Conf. on Automatic Face and Gesture Recognition FGR'04. 2004.
• [22] Ekenel H. K., Stiefelhagen R.: Analysis of Local Appearance-Based Face Recognition: Effects of Feature Selection and Feature Normalization. IEEE Computer Society CVPRW'06, 34-40, 2006.
• [23] Pham-Ngoc P.-T., Jo K.-H.: Multi-face Detection System in Video Sequence, IEEE The 1st International Forum on Strategic Technology, 146-150, 2006.