American Journal of Artificial Intelligence

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Human Face Detection Using Skin Color Segmentation and Watershed Algorithm

Received: 15 May 2017    Accepted: 06 June 2017    Published: 24 July 2017
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Abstract

Face detection receives immense interest in computer vision to improve security and authenticity of a particular system. Color provides useful information at the early stage of face detection in a complex view. Such detection involves many complexities such as background, illumination, and poses. This study provides depth analysis on most prominent color models. The use of those color models can handle well-defined problems in face detection such as occlusions, poses, and illumination conditions. The application areas, techniques used, remarks as well as statistical conversion of the color models from Red Green Blue (RGB) color model are demonstrated. Moreover, a new framework for efficient face detection using skin color segmentation is proposed. The process involves transforming the face images from RGB to the selected color models; then segmentation is carried out by selecting a threshold value for each of the color models. Watershed algorithm is applied to isolate the facial feature from the background. Finally, lips area is localized as it may be missing during the detection process. Detection rate of up to 97.22% was obtained using standard database. The proposed framework targets a range of applications such as PC login security, passport authentication, and pornography filtering.

DOI 10.11648/j.ajai.20170101.14
Published in American Journal of Artificial Intelligence (Volume 1, Issue 1, December 2017)
Page(s) 29-35
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Face Detection, Color Model, Watershed Algorithm, RGB

References
[1] Singh, P., Singh, S. K. & Gaba, N. (2015). YCbCr Technique based Human Face Recognition. International Journal of Advance Research and Innovation, 3 (1): 171 – 174.
[2] Kumar, A. (2014). An Empirical Study of Selection of the Appropriate Color Space for Skin Detection. In International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), 725–730.
[3] Liao, W. H. & Liu, M. J. (2004). Robust Swimming Style Classification from Color Video. In Proceedings of the International Computer Symposium, 541–546.
[4] Arentz, W. A. & Olstad, B. (1994). Classifying Offensive Sites based on Image Content. Computer Vision and Image Understamding, 295–310.
[5] Duan, G. L., Cui, W. & Zhang, H. (2002). Adult Image Detection Method based on Skin Color Model and Support Vector Machine. In Proceedings of the 5th Asian conference on computer vision, 797–800.
[6] Jones, M. J. & Rehg, J. M. (2002). Statistical Color Models with Application to Skin Detection. In International Journal of Computer Vision, 81–96.
[7] Zeng, W., Gao, W., Zhang, T. & Liu, Y. (2004). Image Guarder: An Intelligent Detector for Adult Images. In Proceedings of the Asian Conference on Computer Vision, 198–203.
[8] Bosson, G. Cawley, Y. C. & Harvey, (2002). Non-retrieval: Blocking Pornographic Images. In Proceedings of the International Conference on Image and Video Retrieval, 50–60.
[9] Zhengming, L, Zhan, T. & Zhang, J. (2010). Skin Detection in Color Images. In IEEE Computer Engineering and Technology (ICCET), 2010 2nd International Conference on, 1.
[10] Yang, J., Fu, Z., Tan, T. & Hu, W. (2004). A Novel Approach to Detecting Adult Images. In Proceedings of the 17th International Conference on Pattern Recognition, vol 4, pp 479–482.
[11] Phung, S. L. Bouzerdoum, A. & Chai, D. (2005). Skin Segmentation using Color Pixel Classification: Analysis and Comparison. In IEEE Transaction on Pattern Analysis and Machine Intelligence, 27 (1).
[12] Qiong, L. & Guang-Zheng, P. (2010). A Robust Skin Color Based Face Detection Algorithm. In 2nd International Asia Conference on Informatics in Control, Automation and Robotics.
[13] Zhengzhen, Z. & Yuexiang, S. (2009). Skin Color Detecting unite YCgCb color space with YCgCr color space. In Image Analysis and Signal Processing, 2009. International Conference, 221-225.
[14] Fatma S. M., Abdulganiyu A. Y. & Zahraddeen S. (2015). Evaluation of Suitable Color Model for Human Face Detection. International Journal of Advanced and Applied Sciences. 2 (12), 46-50.
[15] Liu, C. C. (2012). A Global Color Transfer Scheme Between Images based On Multiple Regression Analysis. International Journal of Innovative Computing, Information and Control, 8 (1A): 167-186.
[16] Chaves-Gonzlez, J. M., Vega-Rodguez, M. A, Gomez-Pulido, J. A. & Snchez-Pérez, J. M. (2010). Detecting Skin in Face Recognition Systems: A colour Spaces Study, Digital Signal Processing, 806–823.
[17] Ankit, C. & Neha, S. (2013). A Robust Rapid Approach to Image Segmentation with Optimal Thresholding and Watershed Transform. International Journal of Computer Applications. 65 (9), 1-7.
[18] Ali, A. & Sedigheh, G. (2011). Robust Component-based Face Detection Using Color Feature. In Proceedings of the World Congress on Engineering, London, UK, 2, 2-6.
[19] Ekta, R. & Saroj, K. L. (2013). Comparative Analysis of Skin Color Based Models for Face Detection. Signal & Image Processing: An International Journal (SIPIJ), 4 (2), 69-75.
[20] Kumar, A. (2014). An Empirical Study of Selection of the Appropriate Color Space for Skin Detection. In International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), 725–730.
[21] Chandrashekar M. B. (2012). Automated Face Detection in Color Images using Skin Region and Adaptive Template Matching. International Journal of Computer and Electronics Research. 1 (3), 106-110.
Author Information
  • National Biotechnology Development Agency (NABDA), Abuja, Nigeria

  • Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (Uni SZA), Terengganu, Malaysia

  • Department of Computer Science, Federal University Dutse, Jigawa State, Nigeria

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  • APA Style

    Abdulganiyu Abdu Yusuf, Fatma Susilawati Mohamad, Zahraddeen Sufyanu. (2017). Human Face Detection Using Skin Color Segmentation and Watershed Algorithm. American Journal of Artificial Intelligence, 1(1), 29-35. https://doi.org/10.11648/j.ajai.20170101.14

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    ACS Style

    Abdulganiyu Abdu Yusuf; Fatma Susilawati Mohamad; Zahraddeen Sufyanu. Human Face Detection Using Skin Color Segmentation and Watershed Algorithm. Am. J. Artif. Intell. 2017, 1(1), 29-35. doi: 10.11648/j.ajai.20170101.14

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    AMA Style

    Abdulganiyu Abdu Yusuf, Fatma Susilawati Mohamad, Zahraddeen Sufyanu. Human Face Detection Using Skin Color Segmentation and Watershed Algorithm. Am J Artif Intell. 2017;1(1):29-35. doi: 10.11648/j.ajai.20170101.14

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  • @article{10.11648/j.ajai.20170101.14,
      author = {Abdulganiyu Abdu Yusuf and Fatma Susilawati Mohamad and Zahraddeen Sufyanu},
      title = {Human Face Detection Using Skin Color Segmentation and Watershed Algorithm},
      journal = {American Journal of Artificial Intelligence},
      volume = {1},
      number = {1},
      pages = {29-35},
      doi = {10.11648/j.ajai.20170101.14},
      url = {https://doi.org/10.11648/j.ajai.20170101.14},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajai.20170101.14},
      abstract = {Face detection receives immense interest in computer vision to improve security and authenticity of a particular system. Color provides useful information at the early stage of face detection in a complex view. Such detection involves many complexities such as background, illumination, and poses. This study provides depth analysis on most prominent color models. The use of those color models can handle well-defined problems in face detection such as occlusions, poses, and illumination conditions. The application areas, techniques used, remarks as well as statistical conversion of the color models from Red Green Blue (RGB) color model are demonstrated. Moreover, a new framework for efficient face detection using skin color segmentation is proposed. The process involves transforming the face images from RGB to the selected color models; then segmentation is carried out by selecting a threshold value for each of the color models. Watershed algorithm is applied to isolate the facial feature from the background. Finally, lips area is localized as it may be missing during the detection process. Detection rate of up to 97.22% was obtained using standard database. The proposed framework targets a range of applications such as PC login security, passport authentication, and pornography filtering.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Human Face Detection Using Skin Color Segmentation and Watershed Algorithm
    AU  - Abdulganiyu Abdu Yusuf
    AU  - Fatma Susilawati Mohamad
    AU  - Zahraddeen Sufyanu
    Y1  - 2017/07/24
    PY  - 2017
    N1  - https://doi.org/10.11648/j.ajai.20170101.14
    DO  - 10.11648/j.ajai.20170101.14
    T2  - American Journal of Artificial Intelligence
    JF  - American Journal of Artificial Intelligence
    JO  - American Journal of Artificial Intelligence
    SP  - 29
    EP  - 35
    PB  - Science Publishing Group
    SN  - 2639-9733
    UR  - https://doi.org/10.11648/j.ajai.20170101.14
    AB  - Face detection receives immense interest in computer vision to improve security and authenticity of a particular system. Color provides useful information at the early stage of face detection in a complex view. Such detection involves many complexities such as background, illumination, and poses. This study provides depth analysis on most prominent color models. The use of those color models can handle well-defined problems in face detection such as occlusions, poses, and illumination conditions. The application areas, techniques used, remarks as well as statistical conversion of the color models from Red Green Blue (RGB) color model are demonstrated. Moreover, a new framework for efficient face detection using skin color segmentation is proposed. The process involves transforming the face images from RGB to the selected color models; then segmentation is carried out by selecting a threshold value for each of the color models. Watershed algorithm is applied to isolate the facial feature from the background. Finally, lips area is localized as it may be missing during the detection process. Detection rate of up to 97.22% was obtained using standard database. The proposed framework targets a range of applications such as PC login security, passport authentication, and pornography filtering.
    VL  - 1
    IS  - 1
    ER  - 

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