Advertisement

Improved Human Skin Segmentation Using Fuzzy Fusion Based on Optimized Thresholds by Genetic Algorithms

  • Anderson SantosEmail author
  • Jônatas Paiva
  • Claudio Toledo
  • Helio Pedrini
Chapter

Abstract

Human skin segmentation has several applications in computer vision beyond its main purpose of distinguishing between skin and nonskin regions. Despite the large number of methods available in the literature, accurate skin segmentation is still a challenging task. Many methods rely only on color information, which does not completely discriminate the image regions due to variations in lighting conditions and ambiguity between skin and background color. This chapter extends upon a self-contained method for skin segmentation that outlines regions from which the overall skin color can be estimated and such that the color model is adjusted to a particular image. This process is based on thresholds that were empirically defined in a first approach. The proposed method has three main contributions over the previous one. First, genetic algorithm (GA) is applied to search for better thresholds that will be used to extract appropriate seeds from the general probability and texture maps. Next, the GA is also applied to define thresholds for edge detectors aiming to improve edge connections. Finally, a fuzzy method for fusion is included where its parameters are optimized by GA during a learning phase. The improvements added to the skin segmentation method are evaluated on a set of hand gesture images. A statistical analysis is conducted over the computational results achieved by each evaluated method, indicating a superior performance of our novel skin segmentation method.

Keywords

Human skin segmentation Fuzzy fusion Self-adaptation skin segmentation Genetic algorithms 

Notes

Acknowledgments

The authors are thankful to FAPESP (grant #2011/22749-8) and CNPq (grant #307113/2012-4) for their financial support.

References

  1. 1.
    Hu, X., Peng, S., Yan, J., Zhang, N.: Fast face detection based on skin color segmentation using single Chrominance Cr. In: 7th International Congress on Image and Signal Processing, pp. 687–692. IEEE (2014)Google Scholar
  2. 2.
    Ji, S., Lu, X., Xu, Q.: A fast face detection method combining skin color feature and adaboost. In: International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, pp. 1–5. IEEE (2014)Google Scholar
  3. 3.
    Palacios, J.M., Sagüés, C., Montijano, E., Llorente, S.: Human-computer interaction based on hand gestures using RGB-D sensors. Sensors 13(9), 11842–11860 (2013)CrossRefGoogle Scholar
  4. 4.
    Wachs, J.P., Kölsch, M., Stern, H., Edan, Y.: Vision-based hand-gesture applications. Commun. ACM 54(2), 60–71 (2011). FebCrossRefGoogle Scholar
  5. 5.
    Jones, M.J., Rehg, J.M.: Statistical color models with application to skin detection. Int. J. Comput. Vis. 46(1), 81–96 (2002)CrossRefzbMATHGoogle Scholar
  6. 6.
    Vo, D.M., Jiang, L., Zell, A.: Real time person detection and tracking by mobile robots using RGB-D images. In: IEEE International Conference on Robotics and Biomimetics, pp. 689–694. IEEE (2014)Google Scholar
  7. 7.
    Jeong, C.-Y., Kim, J.-S., Hong, K.-S.: Appearance-based nude image detection. In: 17th International Conference on Pattern Recognition, vol. 4, pp. 467–470. IEEE (2004)Google Scholar
  8. 8.
    Platzer, C., Stuetz, M., Lindorfer, M.: Skin sheriff: a machine learning solution for detecting explicit images. In: 2nd International Workshop on Security and Forensics in Communication Systems, pp. 45–56. ACM, New York (2014)Google Scholar
  9. 9.
    Acton, S.T., Rossi, A.: Matching and retrieval of tattoo images: active contour CBIR and glocal image features. In: IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 21–24. IEEE (2008)Google Scholar
  10. 10.
    Choraś, R.S.: CBIR System for detecting and blocking adult images. In: 9th WSEAS International Conference on Signal Processing, pp. 52–57. World Scientific and Engineering Academy and Society, Stevens Point (2010)Google Scholar
  11. 11.
    Manresa-Yee, C., Varona, J., Mas, R., Perales, F.J.: Hand tracking and gesture recognition for human-computer interaction. Progress In Computer Vision And Image, Analysis, pp. 401–412 (2010)Google Scholar
  12. 12.
    Ren, Z., Meng, J., Yuan, J.: Depth camera based hand gesture recognition and its applications in human-computer-interaction. In: 8th International Conference on Information, Communications and Signal Processing, pp. 1–5. IEEE (2011)Google Scholar
  13. 13.
    Santos, A., Pedrini, H.: A Self-adaptation method for human skin segmentation based on seed growing. In: 10th International Conference on Computer Vision Theory and Applications, pp. 455–462. Berlin (2015)Google Scholar
  14. 14.
    Kakumanu, P., Makrogiannis, S., Bourbakis, N.: A survey of skin-color modeling and detection methods. Pattern Recognit. 40(3), 1106–1122 (2007)CrossRefzbMATHGoogle Scholar
  15. 15.
    Kawulok, M., Nalepa, J., Kawulok, J.: Skin detection and segmentation in color images. Advances in Low-Level Color Image Processing, pp. 329–366. Springer, Berlin (2014)Google Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    Zarit, B.D., Super, B.J., Quek, F.K.: Comparison of five color models in skin pixel classification. In: International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, pp. 58–63 (1999)Google Scholar
  18. 18.
    Sobottka, K., Pitas, I.: Face localization and facial feature extraction based on shape and color information. In: International Conference on Image Processing, vol. 3, pp. 483–486. IEEE (1996)Google Scholar
  19. 19.
    Cheddad, A., Condell, J., Curran, K., Mc Kevitt, P.: A skin tone detection algorithm for an adaptive approach to steganography. Signal Process. 89(12), 2465–2478 (2009)Google Scholar
  20. 20.
    Hsu, R.-L., Abdel-Mottaleb, M., Jain, A.K.: Face detection in color images. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 696–706 (2002)CrossRefGoogle Scholar
  21. 21.
    Soriano, M., Martinkauppi, B., Huovinen, S., Laaksonen, M.: Skin detection in video under changing illumination conditions. In 15th International Conference on Pattern Recognition, vol. 1, pp. 839–842. IEEE (2000)Google Scholar
  22. 22.
    Kovac, J., Peer, P., Solina, F.: Human skin color clustering for face detection. In: International Conference on Computer as a Tool. vol. 2, pp. 144–148. IEEE (2003)Google Scholar
  23. 23.
    Phung, S.L., Chai, D., Bouzerdoum, A.: Adaptive skin segmentation in color images. In: International Conference on Multimedia and Expo, vol. 3, pp. 111–173 (2003)Google Scholar
  24. 24.
    Fritsch, J., Lang, S., Kleinehagenbrock, M., Fink, G.A., Sagerer, G.: Improving adaptive skin color segmentation by incorporating results from face detection. In: 11th IEEE International Workshop on Robot and Human Interactive Communication, pp. 337–343 (2002)Google Scholar
  25. 25.
    Taylor, M.J., Morris, T.: Adaptive skin segmentation via feature-based face detection. In: SPIE Photonics Europe, p. 91390P. International Society for Optics and Photonics (2014)Google Scholar
  26. 26.
    Kawulok, M.: Energy-based blob analysis for improving precision of skin segmentation. Multimed. Tools Appl. 49(3), 463–481 (2010)CrossRefGoogle Scholar
  27. 27.
    Kawulok, M.: Fast propagation-based skin regions segmentation in color images. In: 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, pp. 1–7 (2013)Google Scholar
  28. 28.
    Ruiz-del Solar, J., Verschae, R.: Skin Detection using Neighborhood Information. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 463–468. IEEE (2004)Google Scholar
  29. 29.
    Dijkstra, E.W.: A note on two problems in connection with graphs. Numer. Math. 1, 269–271 (1959)MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Wang, X., Zhang, X., Yao, J.: Skin color detection under complex background. In: International Conference on Mechatronic Science, Electric Engineering and Computer, pp. 1985–1988 (2011)Google Scholar
  31. 31.
    Gonzalez, R., Woods, R., Eddins, S.: Digital Image Processing using MATLAB. Gatesmark Publishing, Knoxville (2009)Google Scholar
  32. 32.
    Schwartz, W.R., Pedrini, H.: Color textured image segmentation based on spatial dependence using 3D co-occurrence matrices and Markov random fields. 15th International Conference in Central Europe on Computer Graphics. Visualization and Computer Vision, pp. 81–87. Czech Republic (2007)Google Scholar
  33. 33.
    Ng, P., Chi-Man, P.: Skin color segmentation by texture feature extraction and K-means clustering. In: Third International Conference on Computational Intelligence, Communication Systems and Networks, pp. 213–218. IEEE (2011)Google Scholar
  34. 34.
    Jiang, Z., Yao, M., Jiang, W.: Skin detection using color, texture and space information. Fourth Int. Conf. Fuzzy Syst. Knowl. Discov. 3, 366–370 (2007)CrossRefGoogle Scholar
  35. 35.
    Gupta, V., Chan, C.C., Sian, P.T.: A differential evolution approach to PET image de-noising. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4173–4176 (2007)Google Scholar
  36. 36.
    Thavavel, V., Basha, J.J., Krishna, M., Murugesan, R.: Heuristic wavelet approach for low-dose EPR tomographic reconstruction: an applicability analysis with phantom and in vivo imaging. Expert Syst. Appl. 39(5), 5717–5726 (2012)CrossRefGoogle Scholar
  37. 37.
    Mukhopadhyay, S., Mandal, J.: Wavelet based denoising of medical images using sub-band adaptive thresholding through genetic algorithm. Procedia Technol. 10, 680–689 (2013) (First International Conference on Computational Intelligence: Modeling Techniques and Applications)Google Scholar
  38. 38.
    Xie, F., Bovik, A.C.: Automatic segmentation of dermoscopy images using self-generating neural networks seeded by genetic algorithm. Pattern Recognit. 46(3), 1012–1019 (2013)CrossRefGoogle Scholar
  39. 39.
    Razmjooy, N., Mousavi, B.S., Soleymani, F.: A hybrid neural network imperialist competitive algorithm for skin color segmentation. Math. Comput. Model. 57(3–4), 848–856 (2013)CrossRefGoogle Scholar
  40. 40.
    Chahir, Y., Elmoataz, A.: Skin-color detection using fuzzy clustering. Int. Symp. Commun. Control Signal Process. 3(1), 1–4 (2006)Google Scholar
  41. 41.
    Hmida, M.B., Jemaa, Y.B.: Fuzzy classification, image segmentation and shape analysis for human face detection. In: 13th IEEE International Conference on Electronics, Circuits and Systems, pp. 640–643. IEEE (2006)Google Scholar
  42. 42.
    Kim, M.H., Park, J.B., Joo, Y.H.: New fuzzy skin model for face detection. Advances in Artificial Intelligence, pp. 557–566. Springer, Berlin (2005)Google Scholar
  43. 43.
    Soille, P.: Morphological Image Analysis: Principles and Applications. Springer Science & Business Media, Berlin (2013)Google Scholar
  44. 44.
    Laws, K.I.: Rapid texture identification. In: 24th Annual Technical Symposium, International Society for Optics and Photonics, pp. 376–381 (1980)Google Scholar
  45. 45.
    Santos, A., Pedrini, H.: Human skin segmentation improved by texture energy under superpixels. In: Pardo, A., Kittler, J. (eds.) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Volume 9423 of Lecture Notes in Computer Science, pp. 35–42. Springer International Publishing, Berlin (2015)Google Scholar
  46. 46.
    Pratt, W.K.: Digital Image Processing. Wiley-Interscience, New York (2001)Google Scholar
  47. 47.
    Kitchen, L., Rosenfeld, A.: Edge evaluation using local edge coherence. IEEE Trans. Syst. Man Cybern. 11(9), 597–605 (1981)CrossRefGoogle Scholar
  48. 48.
    Zhu, Q.: Efficient evaluations of edge connectivity and width uniformity. Image Vis. Comput. 14(1), 21–34 (1996) (Image and Vision Computing Journal on Vision-Based Aids for the Disabled)Google Scholar
  49. 49.
    Tao, C., Xiankun, S., Hua, H., Xiaoming, Y.: Image edge detection based on ACO-PSO algorithm. Int. J. Adv. Comput. Sci. Appl. 6(7), 47–54 (2015)Google Scholar
  50. 50.
    Soria-Frisch, A.: Soft Data Fusion for Computer Vision. Fraunhofer-IRB-Verlag (2004)Google Scholar
  51. 51.
    Murofushi, T., Sugeno, M.: Fuzzy measures and fuzzy integrals. In: Grabisch, M., Murofushi, T., Sugeno, M. (eds.) Fuzzy Measures and Integrals - Theory and Applications, pp. 3–41. Physica Verlag, Heidelberg (2000)Google Scholar
  52. 52.
    Murofushi, T., Sugeno, M.: An interpretation of fuzzy measures and the Choquet integral as an integral with respect to a fuzzy measure. Fuzzy Sets Syst. 29(2), 201–227 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  53. 53.
    Soria-Frisch, A., Verschae, R., Olano, A.: Fuzzy fusion for skin detection. Fuzzy Sets Syst. 158(3), 325–336 (2007)MathSciNetCrossRefGoogle Scholar
  54. 54.
    Tahani, H., Keller, J.M.: Information fusion in computer vision using the fuzzy integral. IEEE Trans. Syst. Man Cybern. 20(3), 733–741 (1990)CrossRefGoogle Scholar
  55. 55.
    Global Optimization Toolbox. http://www.mathworks.com/products/global-optimization/ (2016). Accessed 24 Feb 2016
  56. 56.
    Baker, J.E.: Reducing bias and inefficiency in the selection algorithm. In: Second International Conference on Genetic Algorithms on Genetic Algorithms and Their Application, pp. 14–21. L. Erlbaum Associates Inc., Hillsdale (1987)Google Scholar
  57. 57.
    Kawulok, M., Kawulok, J., Nalepa, J.: Spatial-based skin detection using discriminative skin-presence features. Pattern Recognit. Lett. 41, 3–13 (2014)CrossRefGoogle Scholar
  58. 58.
    Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. CRC Press, Boca Raton (2003)Google Scholar
  59. 59.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Anderson Santos
    • 1
    Email author
  • Jônatas Paiva
    • 2
  • Claudio Toledo
    • 2
  • Helio Pedrini
    • 1
  1. 1.Institute of ComputingUniversity of CampinasCampinasBrazil
  2. 2.Institute of Mathematics and Computer ScienceUniversity of São PauloSão CarlosBrazil

Personalised recommendations