Skip to main content
SpringerLink
Log in

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

  • Chapter
  • First Online:
Book cover Hybrid Soft Computing for Image Segmentation

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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. 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. 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)

    Article  Google Scholar 

  4. Wachs, J.P., Kölsch, M., Stern, H., Edan, Y.: Vision-based hand-gesture applications. Commun. ACM 54(2), 60–71 (2011). Feb

    Article  Google Scholar 

  5. Jones, M.J., Rehg, J.M.: Statistical color models with application to skin detection. Int. J. Comput. Vis. 46(1), 81–96 (2002)

    Article  MATH  Google Scholar 

  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. 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. 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. 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. 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. 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. 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. 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. Kakumanu, P., Makrogiannis, S., Bourbakis, N.: A survey of skin-color modeling and detection methods. Pattern Recognit. 40(3), 1106–1122 (2007)

    Article  MATH  Google Scholar 

  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. 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 

  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. 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. 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. 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)

    Article  Google Scholar 

  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. 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. 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. 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. 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. Kawulok, M.: Energy-based blob analysis for improving precision of skin segmentation. Multimed. Tools Appl. 49(3), 463–481 (2010)

    Article  Google Scholar 

  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. 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. Dijkstra, E.W.: A note on two problems in connection with graphs. Numer. Math. 1, 269–271 (1959)

    Article  MathSciNet  MATH  Google Scholar 

  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. Gonzalez, R., Woods, R., Eddins, S.: Digital Image Processing using MATLAB. Gatesmark Publishing, Knoxville (2009)

    Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. Soille, P.: Morphological Image Analysis: Principles and Applications. Springer Science & Business Media, Berlin (2013)

    Google Scholar 

  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. 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. Pratt, W.K.: Digital Image Processing. Wiley-Interscience, New York (2001)

    Google Scholar 

  47. Kitchen, L., Rosenfeld, A.: Edge evaluation using local edge coherence. IEEE Trans. Syst. Man Cybern. 11(9), 597–605 (1981)

    Article  Google Scholar 

  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. 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. Soria-Frisch, A.: Soft Data Fusion for Computer Vision. Fraunhofer-IRB-Verlag (2004)

    Google Scholar 

  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. 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)

    Article  MathSciNet  MATH  Google Scholar 

  53. Soria-Frisch, A., Verschae, R., Olano, A.: Fuzzy fusion for skin detection. Fuzzy Sets Syst. 158(3), 325–336 (2007)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  55. Global Optimization Toolbox. http://www.mathworks.com/products/global-optimization/ (2016). Accessed 24 Feb 2016

  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. Kawulok, M., Kawulok, J., Nalepa, J.: Spatial-based skin detection using discriminative skin-presence features. Pattern Recognit. Lett. 41, 3–13 (2014)

    Article  Google Scholar 

  58. Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. CRC Press, Boca Raton (2003)

    Google Scholar 

  59. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

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

Author information

Authors and Affiliations

  1. Institute of Computing, University of Campinas, Campinas, SP, 13083-852, Brazil

    Anderson Santos & Helio Pedrini

  2. Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, SP, 13566-590, Brazil

    Jônatas Paiva & Claudio Toledo

Authors
  1. Anderson Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jônatas Paiva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Claudio Toledo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Helio Pedrini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anderson Santos .

Editor information

Editors and Affiliations

  1. Department of Information Technology, RCC Institute of Information Technology, Kolkata, West Bengal, India

    Siddhartha Bhattacharyya

  2. Department of Computer and System Sciences, Visva-Bharati University, Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Computer Science and Engineering, Cooch Behar Govt. Engineering College, Cooch Behar, West Bengal, India

    Sourav De

  4. Department of Strategic Development, Raiffeisenbank Austria, Zagreb, Croatia

    Goran Klepac

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this chapter

Cite this chapter

Santos, A., Paiva, J., Toledo, C., Pedrini, H. (2016). Improved Human Skin Segmentation Using Fuzzy Fusion Based on Optimized Thresholds by Genetic Algorithms. In: Bhattacharyya, S., Dutta, P., De, S., Klepac, G. (eds) Hybrid Soft Computing for Image Segmentation. Springer, Cham. https://doi.org/10.1007/978-3-319-47223-2_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47223-2_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47222-5

  • Online ISBN: 978-3-319-47223-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation