Skip to main content
SpringerLink
Log in

Skin Identification Using Deep Convolutional Neural Network

  • Conference paper
  • First Online:
Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11844))

Included in the following conference series:

Abstract

Skin identification can be used in several security applications such as border’s security checkpoints and facial recognition in bio-metric systems. Traditional skin identification techniques were unable to deal with the high complexity and uncertainty of human skin in uncontrolled environments. To address this gap, this research proposes a new skin identification technique using deep convolutional neural network. The proposed sequential deep model consists of three blocks of convolutional layers, followed by a series of fully connected layers, optimized to maximize skin texture classification accuracy. The proposed model performance has been compared with some of the well-known texture-based skin identification techniques and delivered superior results in terms of overall accuracy. The experiments were carried out over two datasets including FSD Benchmark dataset as well as an in-house skin texture patch dataset. Results show that the proposed deep skin identification model with highest reported accuracy of 0.932 and minimum loss of 0.224 delivers reliable and robust skin identification.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Bae, H.J., Jung, S.H.: Image retrieval using texture based on DCT. In: Proceedings of 1997 International Conference on Information, Communications and Signal Processing, ICICS 1997, vol. 2, pp. 1065–1068. IEEE (1997)

    Google Scholar 

  2. Bargo, P.R., Kollias, N.: Measurement of skin texture through polarization imaging. Br. J. Dermatol. 162(4), 724–731 (2010)

    Article  Google Scholar 

  3. Chaichulee, S., et al.: Multi-task convolutional neural network for patient detection and skin segmentation in continuous non-contact vital sign monitoring. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 266–272. IEEE (2017)

    Google Scholar 

  4. Du, S.S., Zhai, X., Poczos, B., Singh, A.: Gradient descent provably optimizes over-parameterized neural networks. arXiv preprint arXiv:1810.02054 (2018)

  5. Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)

    Article  Google Scholar 

  6. Han, W.Y., Lee, J.C.: Palm vein recognition using adaptive Gabor filter. Expert Syst. Appl. 39(18), 13225–13234 (2012)

    Article  Google Scholar 

  7. Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786–804 (1979)

    Article  Google Scholar 

  8. Lloyd, K., Marshall, D., Moore, S.C., Rosin, P.L.: Detecting violent crowds using temporal analysis of GLCM texture. arXiv preprint arXiv 1605 (2016)

    Google Scholar 

  9. Mahmoud, M.K.A., Al-Jumaily, A.: A hybrid system for skin lesion detection: based on Gabor wavelet and support vector machine. In: Information Technology: Proceedings of the 2014 International Symposium on Information Technology (ISIT 2014), Dalian, China, 14–16 October 2014, p. 39. CRC Press (2015)

    Google Scholar 

  10. Oghaz, M.M., Maarof, M.A., Rohani, M.F., Zainal, A., Shaid, S.Z.M.: An optimized skin texture model using gray-level co-occurrence matrix. Neural Comput. Appl. 31(6), 1835–1853 (2019)

    Article  Google Scholar 

  11. Oghaz, M.M., Maarof, M.A., Zainal, A., Rohani, M.F., Yaghoubyan, S.H.: A hybrid color space for skin detection using genetic algorithm heuristic search and principal component analysis technique. PLoS ONE 10(8), e0134828 (2015)

    Article  Google Scholar 

  12. Pang, H., Chen, T., Wang, X., Chang, Z., Shao, S., Zhao, J.: Quantitative evaluation methods of skin condition based on texture feature parameters. Saudi J. Biol. Sci. 24(3), 514–518 (2017)

    Article  Google Scholar 

  13. 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 

  14. Raupov, D.S., Myakinin, O.O., Bratchenko, I.A., Zakharov, V.P., Khramov, A.G.: Skin cancer texture analysis of OCT images based on Haralick, fractal dimension, Markov random field features, and the complex directional field features. In: Optics in Health Care and Biomedical Optics VII, vol. 10024, p. 100244I. International Society for Optics and Photonics (2016)

    Google Scholar 

  15. Rubel, A., Lukin, V., Uss, M., Vozel, B., Pogrebnyak, O., Egiazarian, K.: Efficiency of texture image enhancement by DCT-based filtering. Neurocomputing 175, 948–965 (2016)

    Article  Google Scholar 

  16. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  17. Sun, Y., Tistarelli, M., Maltoni, D.: Structural similarity based image quality map for face recognition across plastic surgery. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–8. IEEE (2013)

    Google Scholar 

  18. Torkashvand, F., Fartash, M.: Automatic segmentation of skin lesion using Markov random field. Can. J. Basic Appl. Sci. 3(3), 93–107 (2015)

    Google Scholar 

  19. Yaghoubyan, S.H., Maarof, M.A., Zainal, A., Foâ, M., Oghaz, M.M., et al.: Fast and effective bag-of-visual-word model to pornographic images recognition using the freak descriptor. J. Soft Comput. Decis. Support Syst. 2(6), 27–33 (2015)

    Google Scholar 

  20. Yuan, Y., Chao, M., Lo, Y.C.: Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance. IEEE Trans. Med. Imaging 36(9), 1876–1886 (2017)

    Article  Google Scholar 

  21. Zhang, X., Weng, C., Yu, B., Li, H.: In-vivo differentiation of photo-aged epidermis skin by texture-based classification. In: Optics in Health Care and Biomedical Optics VI, vol. 9268, p. 92682G. International Society for Optics and Photonics (2014)

    Google Scholar 

  22. Zuo, H., Fan, H., Blasch, E., Ling, H.: Combining convolutional and recurrent neural networks for human skin detection. IEEE Signal Process. Lett. 24(3), 289–293 (2017)

    Article  Google Scholar 

Download references

Acknowledgments

This research is supported by the MIDAS project (agreement G5381), under the NATO Science for Peace and Security Programme. The Titan X Pascal GPU used for this research was donated by NVIDIA.

Author information

Authors and Affiliations

  1. Kingston University London, London, UK

    Mahdi Maktab Dar Oghaz, Vasileios Argyriou & Paolo Remagnino

  2. Leeds Beckett University, Leeds, UK

    Dorothy Monekosso

Authors
  1. Mahdi Maktab Dar Oghaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vasileios Argyriou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dorothy Monekosso
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paolo Remagnino
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahdi Maktab Dar Oghaz .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Oghaz, M.M.D., Argyriou, V., Monekosso, D., Remagnino, P. (2019). Skin Identification Using Deep Convolutional Neural Network. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33720-9_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33719-3

  • Online ISBN: 978-3-030-33720-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation