Pornographic Image Recognition Based on Skin Probability and Eigenporn of Skin ROIs Images

I Gede Pasek Suta Wijaya, IBK Widiartha, Sri Endang Arjarwani


The paper proposed a pornographic image recognition using skin probability and principle component analysis (PCA) on YCbCr color space. The pornographic image recognition is defined as a process to classify the image containing and showing genital elements of human body from any kinds of images. This process is hard to be performed because the images have large variability due to poses, lighting, and background variations. The skin probability and holistic feature, which is extracted by YCbCr skin segmentation and PCA, is employed to handle those variability problems. The function of skin segmentation is to determine skin ROI image and skin probability. While the function of PCA is to extract eigenporn of the skin ROIs images and by using the eigenporns the holistic features are determined. The main aim of this research is to optimize the accuracy and false rejection rate of the skin probability and fusion descriptor based recognition system. The experimental result shows that the proposed method can increase the accuracy by about 12% and decrease the FPR and FNR by about 16%, respectively. The proposed method also works fast for recognition, which requires 1.3.second per image.



image recognition; pornographic; pca; skin probability; and holistic features.

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W. Hu, O. Wu, Z. Chen, Z. Fu, and S. Maybank, “Recognition of pornographic web pages by classifying texts and images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1019–1034, 2007.

J. A. Marcial-Basilio, G. Aguilar-Torres, G. Snchez-Prez, L. K. Toscano-Medina, H. M. Prez-Meana, and E. Hernadez, “Explicit content image detection,” Signal & Image Processing : An International Journal (SIPIJ), vol. 1, no. 2, pp. 47–58, 2010.

J. A. Marcial-Basilio, G. Aguilar-Torres, G. Snchez-Prez, L. K. Toscano-Medina, and H. M. Prez-Meana, “Detection of pornographic digital images,” International Journal of Computers, vol. 5, no. 2, pp. 298–305, 2011.

R. Mustafa and D. Zhu, “Objectionable image detection in cloud computing paradigm-a review,” Journal of Computer Science, vol. 9, no. 12, pp. 1715–1721, 2013.

I G. P. S. Wijaya, I B. K.. Widiartha, K. Uchimura, and G. Koutaki, “Phonographic Image Recognition Using Fusion of Scale Invariant Descriptors,” in Proceedings of the 21st Korea-Japan joint Workshop on Frontiers of Computer Vision (FCV 2015), Mokpo, Republic of South Korea, January 2015.

V. Vezhnevets, V. Sazonov, and A. Andreeva, “A survey on pixel-based skin color detection techniques,” Journal of Tractor & Farm Transporter[J], pp. 86–88, 2007.

T. M. Mahmoud, “A new skin color detection technique,” World Academy of Science, Engineering and Technology, pp. 501–505, 2008.

I. G. P. S. Wijaya, I. B. K. Widiartha, and K. Uchimura, “Decreasingfalse positive detection of haar-like based face detection using skin color filtering for crowded face images,” in Proceedings of the 15th Seminar on Intelligent Technology and Its Applications (SITIA 2014), Surabaya Indonesia, 2014.

M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86, 2001.

W. Chen, J. E. Meng, and S. Wu, “Pca and lda in dct domain,” Pattern Recognition Letter, vol. 26, pp. 2474–2482, 2005.

Z. Cuicui, K. Uchimura, C. Zhang, and G. Koutaki, “3d face recognition using multi-level multi-feature fusion,” in Proceedings of the 4th Pacific-Rim Symposium on Image and Video Technology (PSIVT 2010), Singapore, 2010, pp. 21–26.

Z. M. Hafed and M. D. Levine, “Face recognition using the discrete cosine trasnforms,” International Journal of Computer Vision, vol. 43, no. 3, pp. 167–188, 2001.

I. G. P. S. Wijaya, K. Uchimura, and Koutaki, “Face recognition based on incremental predictive linear discriminant analysis,” IEEJ Transactions on Electronics, Information and Systems, vol. 133, no. 1, pp. 74–83, 2013.

Lowe, D.G., 2004, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110.


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