Skip to main content
SpringerLink
Log in

Pixel Classification for Skin Detection in Color Images

  • Chapter
  • First Online:
Advanced Technologies in Practical Applications for National Security

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 106))

Abstract

In this paper a direct, pixel-based skin detection method is proposed and evaluated. Proposed approach discards any spatial information that can be found in digital image and focuses entirely on data-oriented analysis. To ensure the best perfomance two classifiers (Regularized Logistic Regression and Artificial Neural Network with Regularization trained with Backpropagation) were deeply examined, evaluated and compared for this task. The best model achieved the almost perfect accuracy and quality of classification on the used ‘Skin Segmentation Dataset’ provided for the UCI Machine Learning Repository with over 99% accuracy, precision, recall and specificity.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
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. Kakumanu, P., Makrogiannis, S., & Bourbakis, N. (2007). A survey of skin-color modeling and detection methods. Pattern Recognition, 40(3), 1106–1122.

    Article  MATH  Google Scholar 

  2. Khan, Rehanullah, Hanbury, Allan, Stöttinger, Julian, & Bais, Abdul. (2012). Color based skin classification. Pattern Recognition Letters, 33(2), 157–163.

    Article  Google Scholar 

  3. Phung, S. L., Bouzerdoum, A., & Chai, D. (2005). Skin segmentation using color pixel classification: Analysis and comparison. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(1), 148–154.

    Google Scholar 

  4. Kawulok, M., Nalepa, J., & Kawulok, J. (2014). Skin detection and segmentation in color images. In M. Emre Celebi & B. Smolka (Eds.), Advances in low-level color image processing (Vol. 11, pp. 329–366). Lecture notes in computational vision and biomechanics. Netherlands: Springer.

    Google Scholar 

  5. Kawulok, M. (2005). Application of support vector machines in automatic human face recognition. Medical Informatics & Technology (MIT), 9, 143–150.

    Google Scholar 

  6. Chaves-González, Jose M., Vega-Rodríguez, Miguel A., Gómez-Pulido, Juan A., & Sánchez-Pérez, Juan M. (2010). Detecting skin in face recognition systems: A colour spaces study. Digital Signal Processing, 20(3), 806–823.

    Google Scholar 

  7. Hajraoui, Abdellatif, & Sabri, Mohamed. (2014). Face detection algorithm based on skin detection, watershed method and gabor filters. International Journal of Computer Applications, 94(6), 33–39.

    Article  Google Scholar 

  8. Kawulok, M. (2008). Dynamic skin detection in color images for sign language recognition. Proceedings of the ICISP, LNCS, 5099, 112–119.

    Google Scholar 

  9. Grzejszczak, T., Kawulok, M., & Galuszka, A. (2016). Hand landmarks detection and localization in color images. Multimedia Tools and Applications, 75(23), 16363–16387.

    Google Scholar 

  10. Daniec, K., Jedrasiak, K., Nawrat, A., & Bereska, D. (2013). Gyro-stabilized platform for multispectral image acquisition. In A. Nawrat & Z. Kuś (Eds.), Vision based systems for UAV applications (pp. 115–121). Springer.

    Google Scholar 

  11. Zarit, B. D., Super, B. J., & Quek, F. K. H. (1999). 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, 1999. Proceedings (pp. 58–63). IEEE.

    Google Scholar 

  12. Lee, J. S., et al. (2007). Naked image detection based on adaptive and extensible skin color model. Pattern Recognition, 40(8), 2261–2270.

    Article  MATH  Google Scholar 

  13. Chen, M. J., Chi, M. C., Hsu, C. T., & Chen, J. W. (2003). ROI video coding based on H.263+ with robust skin-color detection technique. IEEE Transactions on Consumer Electronics, 49(3), 724–730.

    Article  Google Scholar 

  14. Chai, D., & Bouzerdoum, A. (2000). A Bayesian approach to skin color classification in YCbCr color space. In TENCON 2000. Proceedings (Vol. 2, pp. 421–424). IEEE.

    Google Scholar 

  15. Palus H. (2006). Color image segmentation: Selected techniques. In R. Lukac & K. N. Plataniotis (Eds.), Color image processing: Methods and applications (pp. 103–128). Boca Raton: CRC Press.

    Google Scholar 

  16. Palus, H. (1992). Colour spaces in computer vision. Machine Graphics and Vision, 1(3), 543–554.

    Google Scholar 

  17. Al-Mohair, K., & Suandi, S. A. (2012). Human skin color detection: A review on neural network perspective. International Journal of Innovative Computing, Information and Control, 8(12), 8115–8131.

    Google Scholar 

  18. Świtoński, A., Josiński, H., Jȩdrasiak, K., Polański, A., & Wojciechowski, K. (2010). Classification of poses and movement phases. Computer Vision and Graphics, 193–200.

    Google Scholar 

  19. Ryt, A., Sobel, D., Kwiatkowski, J., Domzal, M., Jedrasiak, K., & Nawrat, A. (2014, September). Real-time laser point tracking. In International Conference on Computer Vision and Graphics (pp. 542–551). Springer: Cham.

    Google Scholar 

  20. Sobel, D., Jȩdrasiak, K., Daniec, K., Wrona, J., Jurgaś, P., & Nawrat, A. M. (2014). Camera calibration for tracked vehicles augmented reality applications. In Innovative control systems for tracked vehicle platforms (pp. 147–162). Springer International Publishing.

    Google Scholar 

  21. Jedrasiak, K., & Nawrat, A. (2008). Fast colour recognition algorithm for robotics. Problemy Eksploatacji, 3, 69–76.

    Google Scholar 

  22. Daniec, K., Iwaneczko, P., Jȩdrasiak, K., & Nawrat, A. (2013). Prototyping the autonomous flight algorithms using the Prepar3D® simulator. In Vision based systems for UAV applications (pp. 219–232). Springer International Publishing.

    Google Scholar 

  23. Bhatt, R., & Dhall, A. (2010). Skin Segmentation Dataset, UCI Machine Learning repository.

    Google Scholar 

  24. Du, K.-L., & Swamy, M. N. S. (2013). Neural networks and statistical learning. Springer Science & Business Media.

    Google Scholar 

  25. Werbos, P. J. (1994). The roots of backpropagation: From ordered derivatives to neural networks and political forecasting (Vol. 1). Wiley.

    Google Scholar 

  26. Kasinski, A., Florek, A., & Schmidt, A. (2008). The PUT face database. Image Processing and Communications, 13(3–4), 59–64.

    Google Scholar 

  27. Jaccard, P. (1912). The distribution of the flora in the Alpine Zone. New Phytologist, 11(2), 37–50.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Polish Ministry for Science and Higher Education under internal grant BKM/514/RAu1/2015/t-21 for Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland.

Author information

Authors and Affiliations

  1. Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Bartosz Binias, Mariusz Frąckiewicz, Krzysztof Jaskot & Henryk Palus

Authors
  1. Bartosz Binias
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mariusz Frąckiewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Krzysztof Jaskot
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Henryk Palus
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bartosz Binias .

Editor information

Editors and Affiliations

  1. Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland

    Aleksander Nawrat

  2. Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland

    Damian Bereska

  3. Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland

    Karol Jędrasiak

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Binias, B., Frąckiewicz, M., Jaskot, K., Palus, H. (2018). Pixel Classification for Skin Detection in Color Images. In: Nawrat, A., Bereska, D., Jędrasiak, K. (eds) Advanced Technologies in Practical Applications for National Security. Studies in Systems, Decision and Control, vol 106. Springer, Cham. https://doi.org/10.1007/978-3-319-64674-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-64674-9_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64673-2

  • Online ISBN: 978-3-319-64674-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation