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A new dynamic cellular learning automata-based skin detector

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Abstract

Skin detection is a difficult and primary task in many image processing applications. Because of the diversity of various image processing tasks, there exists no optimum method that can perform properly for all applications. In this paper, we have proposed a novel skin detection algorithm that combines color and texture information of skin with cellular learning automata to detect skin-like regions in color images. Skin color regions are first detected, by using a committee structure, from among several explicit boundary skin models. Detected skin-color regions are then fed to a texture analyzer which extracts texture features via their color statistical properties and maps them to a skin probability map. This map is then used by cellular learning automata to adaptively make a decision on skin regions. Conducted experiments show that the proposed algorithm achieves the true positive rate of about 86.3% and the false positive rate of about 9.2% on Compaq skin database which shows its efficiency.

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References

  1. Wang, D., Ren, J., Jiang, J., Ipson, S.S.: Skin detection from different color spaces for model-based face detection. Advanced Intelligent Computing Theories and Applications with Aspects of Contemporary Intelligent Computing Techniques, vol. 15, part 14, pp. 487–494. Springer, Berlin (2008)

  2. Huang, F.J., Chen, T.: Tracking of multiple faces for human- computer interfaces and virtual environments. In: Proc. IEEE Int. Conf. Multimedia and Expo, New York, pp. 1563–1566 (2000)

  3. Ho, W., Watters, P.: Statistical and structural approaches to filtering internet pornography. In: Proc. IEEE Int. Conf. on Systems, Man and Cybernetics, pp. 4792–4798 (2004)

  4. Sebe N., Cohen I., Gozman F.G., Gevers T., Huang T.S.: Learning probabilistic classifiers for human-computer interaction applications. Multimed. Syst. Special Issue Syst. Archit. Multimed. Inf. Retr. 10(6), 484–498 (2005)

    Google Scholar 

  5. Park, S., Aggarwal, J.K.: A hierarchical Bayesian network for event recognition of human actions and interactions. In: ACM Multimedia Systems Journal, Special Issue on Video Surveillance, pp. 164–179 (2004)

  6. Yang B., Hurson A.R.: Similarity-based clustering strategy for mobile ad hoc multimedia databases. Mobile Inf. Syst. 1(4), 253–273 (2005)

    Google Scholar 

  7. Available online at http://www.idigitalemotion.com/tutorials/guest/skin_tone/skintone.html

  8. Shin, M.C., Chang, K., TSAP L.V.: Does color space transformation make any difference on skin detection? In: Proc. IEEE Workshop on Applications of Computer, USA, pp. 275–279 (2002)

  9. Albiol A., Torres L., Delp E.J.: Optimum color space for skin detection. Int. Conf. Image Process. (ICIP) 1, 122–124 (2001)

    Google Scholar 

  10. Pung S.P., Bouzerdoum A., Chai D.: Skin segmentation using color pixel classification: analysis and comparison. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 148–154 (2005)

    Article  Google Scholar 

  11. Vezhnevets, V., Andreeva, A.: A comparative assessment of pixel-based skin detection methods. Technical report, Graphics and Media Laboratory, Moscow State University (2005)

  12. Kakumanu P., Makrogiannis S., Bourbakis N.: A survey of skin-color modeling and detection methods. J. Pattern Recognit. 40, 1106–1122 (2007)

    Article  MATH  Google Scholar 

  13. Vezhnevets, V., Sazonov, V., Andreeva, A.: A survey on pixel-based skin color detection techniques. GRAPHICON03, pp. 85–92 (2003)

  14. Abin, A.A., Fotouhi, M., Kasaei, S.: Skin segmentation based on cellular learning automata. In: Proc. Advances in Mobile Computing and Multimedia, Linz, Austria, pp. 254–259 (2008)

  15. Beigy H., Meybodi M.R.: Asynchronous cellular learning automata. J. Auomatica 44(4), 1350–1357 (2008)

    Article  MathSciNet  Google Scholar 

  16. Meybodi, M.R., Khojasteh, M.R.: Application of cellular learning automata in modeling of commerce networks. In: Proc. 6th Annual International Computer Society of Iran Computer Conference (CSICC). Isfahan, Iran, pp. 284–295 (2001)

  17. Dommen B.J., Croix D.S.: Graph partitioning using learning automata. IEEE Trans. Comput. 45, 195–208 (1996)

    Article  MathSciNet  Google Scholar 

  18. Dommen B.J., Roberts T.D.: Continuous learning automata solutions to the capacity assignment problem. IEEE Trans. Comput. 49, 608–620 (2000)

    Article  Google Scholar 

  19. Meybodi M.R., Beigy H.: A note on learning automata-based schemes for adaptation of BP parameters. J. Neurocomputing 48, 957–974 (2002)

    Article  MATH  Google Scholar 

  20. Gomez, G., Morales, E.: Automatic feature construction and a simple rule induction algorithm for skin detection. In: Proc. of the ICML Workshop on Machine Learning in Computer Vision, pp. 31–38 (2002)

  21. Littmann E., Ritter H.: Adaptive color segmentation: a comparison of neural and statistical methods. IEEE Trans. Neural Netw. 8(1), 175–185 (1997)

    Article  Google Scholar 

  22. Phung S.L., Bouzerdoum A., Chai D.: Skin segmentation using color pixel classification: analysis and comparison. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 148–154 (2005)

    Article  Google Scholar 

  23. Dai Y., Nakano Y.: Face-texture model based on SGLD and its application in face detection in a color scene. Pattern Recognit. 29(6), 1007–1017 (1996)

    Article  Google Scholar 

  24. Zhanwu, X., Miaoliang, Z.: Color-based skin detection survey and evaluation. In: Proc. 12th International Multi-Media Modeling Conference (MMM ‘06), pp. 143–152 (2006)

  25. Jones M., Rehg J.: Statistical color models with application to skin color detection. Proc. Int. J. Comput. Vis. 46, 81–96 (2002)

    Article  MATH  Google Scholar 

  26. Yang J., Lu A., Waibel W.: Skin-color modeling and adaptation. ACCV98 Hong Kong. China 1352, 687–694 (1998)

    Google Scholar 

  27. Wang Y., Yuan B.: A novel approach for human face detection from color images under complex background. Pattern Recognit. 34(10), 1983–1992 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  28. Sobottka K., Pitas I.: A novel method for automatic face segmentation, facial feature extraction and tracking. Signal Process. Image Commun. 12, 263–281 (1998)

    Article  Google Scholar 

  29. Kovac J., Peer P., Solina F.: Human skin color clustering for face detection. Proc. Int. Conf. Comput. Tool 2, 144–148 (2003)

    Google Scholar 

  30. Chai D., Ngan K.N.: Face segmentation using skin-color map in videophone applications. IEEE Trans. Circuits Syst. Video Technol. 9(4), 518–521 (1999)

    Article  Google Scholar 

  31. Brown, D., Craw, I., Lewthwaite, J.: A SOM based approach to skin detection with application in real time systems. In: Proc. British Machine Vision Conference, pp. 491–500 (2001)

  32. Sigal L., Sclaroff S., Athitsos V.: Estimation and prediction of evolving color distributions for skin segmentation under varying illumination. IEEE Conf. Comput. Vision Pattern Recognit. 2, 152–159 (2000)

    Google Scholar 

  33. Soriano, M., Huovinen, S., Martinkauppi, B., Laaksonen, M.: Skin detection in video under changing illumination conditions. In: Proc. 15th International Conference on Pattern Recognition, vol. 1, pp. 839–842 (2000)

  34. Yoo T.W., Oh I.S.: A fast algorithm for tracking human faces based on chromatic histograms. Pattern Recognit. Lett. 20(10), 967–978 (1999)

    Article  Google Scholar 

  35. Chai D., Bouzerdoum A.: A Bayesian approach to skin color classification in ycbcr color space. IEEE Region Ten Conf. 2, 421–424 (2000)

    Google Scholar 

  36. Menser, B., Wien, M.: Segmentation and tracking of facial regions in color image sequences. In: Proc. SPIE Visual Communications and Image Processing, pp. 731–740 (2000)

  37. Kuchi P., Gabbur P., Bhat S., David S.: Human face detection and tracking using skin color modeling and connected component operators. IETE J. Res. Special Issue Visual Media Process. 48(3–4), 289–293 (2002)

    Google Scholar 

  38. Terrillon JC, Shirazi MN, Fukamachi H, Akamatsu S (2000) Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images. In: Proc. International Conference on Face and Gesture Recognition, pp. 54–61

  39. Caetano, T.S., Olabarriaga, S.D., Barone, D.A.C.: Performance evaluation of single and multiple-Gaussian models for skin-color modeling. SIBGRAPI02, pp. 275–282 (2002)

  40. Yang M., Ahuja N.: Gaussian mixture model for human skin color and its application in image and video databases. Proc. SPIE: Conf. Storage Retrieval Image Video Databases (SPIE 99) 3656, 458–466 (1999)

    Google Scholar 

  41. Guillamet, D., Vitria, J.: Skin segmentation using non linear principal component analysis. In: Proc. 2nd Catalan Congress on Artificial Intelligence (CCIA’99). Spain, pp. 224–231 (1999)

  42. Lee, J.Y., Yoo, S.I.: An elliptical boundary model for skin color detection. In: Proc. Int. Conf. on Imaging Science, Systems, and Technology, pp. 579–584 (2002)

  43. Jedynak B., Zheng H., Daoudi M.: Statistical models for skin detection. IEEE Worksh. Stat. Anal. Comput. Vis. 8, 92 (2003)

    Google Scholar 

  44. Phung S.L., Chai D., Bouzerdoum A.: A universal and robust human skin color model using neural networks. IJCNN01 4, 2844–2849 (2001)

    Google Scholar 

  45. Chen C., Chiang S.P.: Detection of human faces in colour images. IEE Proc. Vis. Image Signal Process 144(6), 384–388 (1997)

    Article  Google Scholar 

  46. Sebe N., Cohen T., Huang T.S., Gevers T.: Skin detection, a Bayesian network approach. ICPR04 2, 903–906 (2004)

    Google Scholar 

  47. Juang C.F., Chiu S.H., Shiu S.J.: Fuzzy system learned through fuzzy clustering and support vector machine for human skin color segmentation. IEEE Trans. Syst. Man Cybernetics-Part A: Syst. Humans 37(6), 1077–1087 (2007)

    Article  Google Scholar 

  48. Pujol, F.A., Espi, R., Mora, H., Sanchez, J.L.: A fuzzy approach to skin color detection. MICAI 2008: Advances in Artificial Intelligence, vol. 5317, pp. 532–542. Springer, Berlin (2008)

  49. Diplaros A., Gevers T., Vlassis N.: Skin detection using the EM algorithm with spatial constraints. IEEE Int. Conf. Syst. Man Cybern. 4, 3071–3075 (2004)

    Google Scholar 

  50. Kruppa, H., Bauer, M.A., Schiele, B.: Skin patch detection in real-world images. Annual Symposium for Pattern Recognition of the DAGM. Springer LNCS 2449, vol. 109, pp. 109–116 (2002)

  51. Forsyth D.A., Fleck M.: Automatic detection of human nudes. Int. J. Comput. Vis. 32(1), 63–77 (1999)

    Article  Google Scholar 

  52. Xu, Y., Li, B., Xue, X., Lu, H.: Region-based pornographic image detection. In: IEEE 7th Workshop on Multimedia Signal Processing (MMSP’05). Shanghai, China, pp. 1–4 (2005)

  53. Buchsbaum G.: A spatial processor model for object colour perception. J. Franklin Inst. 310, 1–26 (1990)

    Article  Google Scholar 

  54. Forsyth D.: A novel approach to color constancy. J. Comput. Vis. 5(1), 5–36 (1990)

    Article  Google Scholar 

  55. Brainard D.H., Freeman W.T.: Bayesian color constancy. J. Opt. Soc. Am. 14, 1393–1411 (1997)

    Article  Google Scholar 

  56. Nayak A., Chaudhuri S.: Self-induced color correction for skin tracking under varying illumination. ICIP03 2, 1009–1012 (2003)

    Google Scholar 

  57. Strring M., Koèka T., Anderson H.J., Granum E.: Tracking regions of human skin through illumination changes. Pattern Recognit. Lett. 24(11), 1715–1723 (2003)

    Article  Google Scholar 

  58. Sigal L., Sclaroff S., Atlitsos V.: Skin color-based video segmentation under time-varying illumination. IEEE Trans. PAMI 26(7), 862–877 (2004)

    Google Scholar 

  59. Barnard K., Funt B., Cardei V.: A comparison of computational color constancy algorithms-Part I: theory and experiments with synthetic data. IEEE Trans. Image Process 11(9), 972–984 (2002)

    Article  Google Scholar 

  60. Barnard K., Martin L., Coath A., Funt B.: A comparison of computational color constancy algorithms-Part II: Experiments with image data. IEEE Trans. Image Process 11(9), 985–996 (2002)

    Article  Google Scholar 

  61. Bergasa L.M., Mazo M., Gardel A., Sotelo M.A., Boquete L.: Unsupervised and adaptive Gaussian skin-color model. Image Vis. Comput. 18(12), 987–1003 (2000)

    Article  Google Scholar 

  62. Cho K.M., Jang J.H., Hong K.S.: Adaptive skin-color filter. Pattern Recognit. 34(5), 1067–1073 (2001)

    Article  MATH  Google Scholar 

  63. Frisch, A.S., Verschae, R., Olano, A.: Fuzzy fusion for skin detection, vol. 158, pp. 325–336. Elsevier, Science, Fuzzy Sets and Systems (2007)

  64. Xiao, K., Danghui, L., Lansun, S.: Segmentation of skin color regions based on fuzzy cluster. In: Proc. International Symposium on Intelligent Multimedia, Video and Speech Processing Hong Kong, pp. 125–128 (2004)

  65. George, D.F.J., George, S.E.: Cellular automata cryptography using reconfigurable computing Source. In: Proc. of the 16th International Conference on Developments in Applied Artificial Intelligence, pp. 104–111 (2003)

  66. Mitchell, M.: Computation in cellular automata: a selected review. Technical report, Santa Fe Institute, Santa Fe, NM, USA (1996)

  67. Packard N.H., Wolfram S.: Two-dimensional cellular automata. J. Stat. Phys. 38, 901–946 (1985)

    Article  MATH  MathSciNet  Google Scholar 

  68. Kari, J.: Reversibility of 2D cellular automata is undecidable. Physica, pp. 379–385 (1990)

  69. Tsetlin M.L.: On the behavior of finite automata in random media. Automat. Remote Control 22(10), 1210–1219 (1961)

    MathSciNet  Google Scholar 

  70. Beigy H., Meybodi M.R.: A mathematical framework to study the evolution of cellular learning automata. Adv. Complex Syst. 7(3–4), 295–319 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  71. Narendra K.S., Wright E.A., Mason L.G.: Application of learning automata to telephone traffic routing and control. IEEE Trans. Sys. Man. Cybern. 7(11), 785–792 (1977)

    Article  Google Scholar 

  72. Oommen B.J.: A learning automata solution to the stochastic minimum-spanning circle problem. IEEE Trans. Syst. Man. Cybern. 16, 598–603 (1986)

    Article  MathSciNet  Google Scholar 

  73. Oommen B.J., Raghunath G.: Automata learning and intelligent tertiary searching for stochastic point location. IEEE. Trans. Syst. Man. Cybern. Part B. Cybern. 28(6), 947–954 (1998)

    Article  Google Scholar 

  74. Thathachar M.A.L., Sastry P.S.: Learning optimal discriminant functions through a cooperative game of automata. IEEE Trans. Syst. Man. Cybern. 7(1), 73–85 (1987)

    Article  MathSciNet  Google Scholar 

  75. Meybodi M.R., Kharazmi M.R.: Application of cellular learning automata to image processing. J. Amirkabir 14(56), 1101–1126 (2004)

    Google Scholar 

  76. Beigy H., Meybodi M.R.: Open synchronous cellular learning automata. Adv. Complex Syst. 10(4), 527–556 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  77. Kittler J., Hatef M., Duin R.P.W., Matas J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20, 226–239 (1998)

    Article  Google Scholar 

  78. Haykin, S.: Neural Networks: a Comprehensive Foundation. Prentice Hall PTR Upper Saddle River, NJ (1998)

  79. Abadpour A., Kasaei S.: Pixel–based skin detection for pornography filtering. Iranian J. Electr. Electron. Eng. 1(3), 21–41 (2005)

    Google Scholar 

  80. Gasparini, F., Corchs, S., Schettini, R.: Pixel-based skin colour classification exploiting explicit skin cluster definition methods. In: Proc. 10th Congress of the International Colour Association, vol. 1, pp. 543–546 (2005)

  81. Heieh I.S., Fan K.C., Lin C.: A statistical approach to the detection of human faces in colour nature scene. Pattern Recognit. 35, 1583–1596 (2002)

    Article  Google Scholar 

  82. Brand, J., Mason, J.: A comparative assessment of three approaches to pixel level human skin detection. ICPR01, pp. 1056–1059 (2000)

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Authors and Affiliations

  1. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Ahmad Ali Abin, Mehran Fotouhi & Shohreh Kasaei

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  1. Ahmad Ali Abin
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  2. Mehran Fotouhi
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  3. Shohreh Kasaei
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Correspondence to Shohreh Kasaei.

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This work was in part supported by a grant from Iran telecommunication research center (ITRC). We also would like to thank Dr. Beigy for his help on the theory and implementation of CLA.

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Abin, A.A., Fotouhi, M. & Kasaei, S. A new dynamic cellular learning automata-based skin detector. Multimedia Systems 15, 309–323 (2009). https://doi.org/10.1007/s00530-009-0165-1

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