Fast pornographic image recognition using compact holistic features and multi-layer neural network

(1) * I Gede Pasek Suta Wijaya Mail (Informatics Engineering Dept., Mataram University, Indonesia)
(2) Ida Bagus Ketut Widiartha Mail (Informatics Engineering Dept., Mataram University, Indonesia)
(3) Keiichi Uchimura Mail (Electrical Engineering and Computer Science Dept., Kumamoto University, Japan)
(4) Muhamad Syamsu Iqbal Mail (Electrical Engineering Dept., Mataram University, Indonesia)
(5) Ario Yudo Husodo Mail (Informatics Engineering Dept., Mataram University, Indonesia)
*corresponding author


The paper presents an alternative fast pornographic image recognition using compact holistic features and multi-layer neural network (MNN). The compact holistic features of pornographic images, which are invariant features against pose and scale, is extracted by shape and frequency analysis on pornographic images under skin region of interests (ROIs). The main objective of this work is to design pornographic recognition scheme which not only can improve performances of existing methods (i.e., methods based on skin probability, scale invariant feature transform, eigenporn, and Multilayer-Perceptron and Neuro-Fuzzy (MP-NF)) but also can works fast for recognition. The experimental outcome display that our proposed system can improve 0.3% of accuracy and reduce 6.60% the false negative rate (FNR) of the best existing method (skin probability and eigenporn on YCbCr, SEP), respectively. Additionally, our proposed method also provides almost similar robust performances to the MP-NF on large size dataset. However, our proposed method needs short recognition time by about 0.021 seconds per image for both tested datasets.


Pornographic image; Recognition system; Neural network; Holistik features; Frequency analysis



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[1] S. M. Kia, H. Rahmani, R. Mortezaei, M. E. Moghaddam, and A. Namazi, “A Novel Scheme for Intelligent Recognition of Pornographic Images,” in Computer Vision and Pattern Recognition, Cornell University Library, 2015, available at:

[2] J. A. Marcial-Basilio, G. Aguilar-Torres, G. Sánchez-Pérez, L. K. Toscano-Medina, and H. M. Pérez-Meana, “Detection of Pornographic Digital Images,” Int. J. Comput., vol. 5, no. 2, pp. 298–305, 2011, available at: Google Scholar.

[3] I. G. P. S. Wijaya, I. B. K. Widiartha, K. Uchimura, and G. Koutaki, “Pornographic image rejection using eigenporn of simplified LDA of skin ROIs images,” in 2015 International Conference on Quality in Research (QiR), 2015, pp. 77–80, doi: 10.1109/QiR.2015.7374899.

[4] C. Ries and R. Lienhart, “A survey on visual adult image recognition,” Multimed. Tools Appl., vol. 69, no. 3, pp. 661–688, 2014, doi: 10.1007/s11042-012-1132-y.

[5] L.-H. Lee and C.-J. Luh, “Generation of pornographic blacklist and its incremental update using an inverse chi-square based method,” Inf. Process. Manag., vol. 44, no. 5, pp. 1698–1706, 2008, doi: 10.1016/j.ipm.2008.05.001.

[6] R. Mustafa and D. Zhu, “Objectionable Image Detection in Cloud Computing Paradigm-A Review,” J. Comput. Sci., vol. 9, no. 12, pp. 1715–1721, 2013, doi: 10.3844/jcssp.2013.1715.1721.

[7] A. A. Zaidan, N. N. Ahmad, H. A. Karim, M. Larbani, B. B. Zaidan, and A. Sali, “On the multi-agent learning neural and Bayesian methods in skin detector and pornography classifier: An automated anti-pornography system,” Neurocomputing, vol. 131, pp. 397–418, 2014 , doi: 10.1016/j.neucom.2013.10.003.

[8] W. Hu, O. Wu, Z. Chen, Z. Fu, and S. Maybank, “Recognition of Pornographic Web Pages by Classifying Texts and Images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 6, pp. 1019–1034, 2007, doi: 10.1109/TPAMI.2007.1133.

[9] J. A. M. Basilio, G. A. Torres, G. S. Pérez, L. K. T. Medina, and H. M. P. Meana, “Explicit image detection using YCbCr space color model as skin detection,” Appl. Math. Comput. Eng., pp. 123-128., 2011, available at: Google Scholar.

[10] J.-L. Shih, C.-H. Lee, and C.-S. Yang, “An adult image identification system employing image retrieval technique,” Pattern Recognit. Lett., vol. 28, no. 16, pp. 2367–2374, 2007, doi: 10.1016/j.patrec.2007.08.002.

[11] M. Mironovova and J. Bíla, “Fast fourier transform for feature extraction and neural network for classification of electrocardiogram signals,” in 2015 Fourth International Conference on Future Generation Communication Technology (FGCT), 2015, pp. 1–6, doi: 10.1109/FGCT.2015.7300244.

[12] W. Yongmao and Z. Shan, “Improved method based on DCT and locality preserving projection for face recognition.,” J. Huazhong Norm. Univ., vol. 48, no. 2, 2014, doi: 10.3724/SP.J.1087.2012.00528.

[13] Z. Abidin and A. Alamsyah, “Wavelet based approach for facial expression recognition,” Int. J. Adv. Intell. Informatics, vol. 1, no. 1, pp. 7–14, 2015, doi: 10.26555/ijain.v1i1.7.

[14] M. Wang, H. Jiang, and Y. Li, “Face Recognition based on DWT/DCT and SVM,” in Computer Application and System Modeling (ICCASM), 2010 International Conference on, 2010, vol. 3, pp. V3--507, doi: 10.1109/ICCASM.2010.5620666.

[15] D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, Nov. 2004, doi: 10.1023/B:VISI.0000029664.99615.94.

[16] W. Chen, M. J. Er, and S. Wu, “PCA and LDA in DCT domain,” Pattern Recognit. Lett., vol. 26, no. 15, pp. 2474–2482, 2005, doi: 10.1016/j.patrec.2005.05.004.

[17] I. G. P. S. Wijaya, I. B. K. Widiartha, and S. E. Arjarwani, “Pornographic Image Recognition Based on Skin Probability and Eigenporn of Skin ROIs Images,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 13, no. 3, pp. 985–995, 2015, doi: 10.12928/telkomnika.v13i3.1476.

[18] I. G. P. S. Wijaya, K. Uchimura, and G. Koutaki, “Pornographic Image Recognition using Eigenporn of HSV Skin Segmented Image,” in Proceeding of the 2015 IEICE General Conference, 2015, doi: 10.1109/QiR.2015.7374899.

[19] X. Ou, H. Ling, H. Yu, P. Li, F. Zou, and S. Liu, “Adult image and video recognition by a deep multicontext network and fine-to-coarse strategy,” ACM Trans. Intell. Syst. Technol., vol. 8, no. 5, p. 68, 2017, doi: 10.1145/3057733.

[20] I. G. P. S. Wijaya, I. B. K. Widiartha, K. Uchimura, and G. Koutaki, “Pornographic Image Recognition Using Fusion of Scale Invariant Descriptors,” in Proc. of the 21st Korea-Japan joint Workshop on Frontiers of Computer Vision (FCV 2015) , doi: 10.1109/FCV.2015.7103754.

[21] W. Chen, M. J. Er, S. Wu, and , DCT, “PCA and LDA in Pattern Recognition Letter, Volume pp.,” vol. 26, pp. 2474–2482, 2005, doi: 10.1016/j.patrec.2005.05.004.

[22] M. Turk and A. Pentland, “Eigenfaces for Recognition,” J. Cogn. Neurosci., vol. 3, no. 1, pp. 71–86, 1991, doi: 10.1162/jocn.1991.3.1.71.

[23] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification. John Wiley & Sons, 2012, available at: Google Scholar.

[24] T. K. Hazra, D. P. Singh, and N. Daga, “Optical character recognition using KNN on custom image dataset,” in 2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), 2017, pp. 110–114, doi: 10.1109/IEMECON.2017.8079572.

[25] Z. Ma and A. Leijon, “Human Skin Color Detection in RGB Space with Bayesian Estimation of Beta Mixture Models,” in Proc. of 18th European Signal Processing Conference (EUSIPCO-2010), 2010, pp. 1204–1208, available at:

[26] V. Vezhnevets, V. Sazonov, and A. Andreeva, “A Survey on Pixel-Based Skin Color Detection Techniques,” in Proc. Graphicon-2003, 2003, pp. 85–92, available at: Google Scholar.

[27] T. M. Mahmoud, “A New Skin Color Detection Technique,” World Acad. Sci. Eng. Technol., vol. 2, no. 7, pp. 434–438, 2008, available at: Google Scholar.

[28] R. C. Gonzalez and R. E. Woods, Digital Image Processing (3rd Edition). Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 2006, doi: available at:

[29] H. Demuth, M. Beale, and M. Hagan, “Neural network toolbox,” 1992, available at: Google Scholar.

[30] B. Sharma and K. Venugopalan, “Comparison of neural network training functions for hematoma classification in brain CT images,” IOSR J. Comput. Eng., vol. 16, no. 1, pp. 31–35, 2014, doi: 10.9790/0661-16123135.

[31] M. Latah, “Human action recognition using support vector machines and 3D convolutional neural networks,” Int. J. Adv. Intell. Informatics, vol. 3, no. 1, pp. 47–55, 2017, doi: 10.26555/ijain.v3i1.89.

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