A Robust and Adaptable Method for Face Detection Based on Color Probabilistic Estimation Technique

Download Full Text
Author(s):
Reza Azad, Fatemeh Davami
Published Date:
November 05, 2013
Issue:
Volume 3, Issue 6
Page(s):
1 - 7
DOI:
10.7815/ijorcs.36.2013.072
Views:
4212
Downloads:
152

Keywords:
face detection, image processing, threshold tuning, gaussian model feature extraction
Citation:
Reza Azad, Fatemeh Davami, "A Robust and Adaptable Method for Face Detection Based on Color Probabilistic Estimation Technique". International Journal of Research in Computer Science, 3 (6): pp. 1-7, November 2013. doi:10.7815/ijorcs.36.2013.072 Other Formats

Abstract

Human face perception is currently an active research area in the computer vision community. Skin detection is one of the most important and primary stages for this purpose. So far, many approaches are proposed to done this case. Near all of these methods have tried to find best match intensity distribution with skin pixels based on popular color spaces such as RGB, HSI or YCBCR. Results show that these methods cannot provide an accurate approach for every kind of skin. In this paper, an approach is proposed to solve this problem using a color probabilistic estimation technique. This approach is including two stages. In the first one, the skin intensity distribution is estimated using some train photos of pure skin, and at the second stage, the skin pixels are detected using Gaussian model and optimal threshold tuning. Then from the skin region facial features have been extracted to get the face from the skin region. In the results section, the proposed approach is applied on FEI database and the accuracy rate reached 99.25%. The proposed approach can be used for all kinds of skin using train stage which is the main advantage among the other advantages, such as Low noise sensitivity and low computational complexity.

  1. S.L. Phung, A. Bouzerdoum, and D. Chai, "Skin Segmentation Using Color and Edge Information", In Proc. of International Symposium on Signal Processing and its Applications, Vol. 1,Paris-France 2003, pp. 525-528.
  2. P. Kakumanu, S. Makrogiannis, and N. Bourbakis, "A Survey of Skin-color Modeling and Detection Methods", Pattern Recognition Vol. 40, pp. 1106 – 1122, 2007, doi: 10.1016/j.patcog.2006.06.010.
  3. L. Sigal, S. Sclaroff, and V. Athitsos, "Skin Color-based Video Segmentation Under time-varying Illumination", IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 26, No. 6, pp. 862-877, 2004, doi: 10.1109/TPAMI.2004.35
  4. R.L. Hsu, M. Abdel-Mottaleb, and A.K. Jain, "Face detection in color Images", IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 24, No. 5, pp. 696–706, 2002
  5. Z. Qiang, Ch. Kwang-Ting, W. Ching-Tung, and W. Yi-Leh, "Adaptive learning of an accurate skin-color model", In Proc. of the Sixth IEEE international conference on automatic face and gesture recognition,Seoul-Korea, 2004, pp. 37-42.
  6. M.H. Yang and N. Ahuja, "Gaussian mixture model for human skin color and its applications in image and video databases," In Proc. of SPIE Storage and Retrieval for Image and Video Databases, pp. 458-466, Jan. 1999.
  7. H. Wu, Q. Chen, and M. Yachida, "Face detection from color images using a fuzzy pattern matching method," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 21, No. 6, pp. 557-563, 1999.
  8. Crowley, J. L. and Coutaz, J., “Vision for Man Machine Interaction,” Robotics and Autonomous Systems, Vol. 19, pp. 347-358, 1997, doi: 10.1016/S0921-8890(96)00061-9.
  9. Cahi, D. and Ngan, K. N., “Face Segmentation Using Skin-Color Map in Videophone Applications,” IEEE Transaction on Circuit and Systems for Video Technology, Vol. 9, pp. 551-564, 1999.
  10. Kjeldsen, R. and Kender., J., “Finding Skin in Color Images,” Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, 1996, pp. 312-317, doi: 10.1109/AFGR.1996.557283
  11. M.A. Akhloufi, X. Maldague and W.B. Larbi, "A New Color-Texture Approach for Industrial Products Inspection", Journal of multimedia, Vol. 3, No. 3, pp. 44-50, July 2008, doi: 10.4304/jmm.3.3.44-50.
  12. H. Y.Patil, S.V.Bharambe, A.G.Kothari and K.M.Bhurchandi, “Face Localization and its Implementation on Embedded Platform”, 3rd IEEE International Advance Computing Conference (IACC), 2013,pp. 741-745, doi: 10.1109/IAdCC.2013.6514319.
  13. R. Azad, H.R. Shayegh, “Novel and Tuneable Method for Skin Detection Based on Hybrid Colour Space and Colour Statistical Features", International Conference on computer, Information Technology and Digital Media (CITADIM 2013), pp. 42-45.
  14. S. K. Singh1, D. S. Chauhan, M. Vatsa, R. Singh, "A Robust Skin Color Based Face Detection Algorithm", Tamkang Journal of Science and Engineering, Vol. 6, No. 4, pp. 227-234, 2003.

  • Azad, Reza, and Babak Azad. "Optimized method for real-time face recognition system based on PCA and multiclass support vector machine." Advances in Computer Science: an International Journal 2.5 (2013): 126-132.
  • Azad, Reza, Eslam Ahmadzadeh, and Babak Azad. "Real-Time Human Face Detection in Noisy Images Based on Skin Color Fusion Model and Eye Detection." Intelligent Computing, Communication and Devices. Springer India, 2015. 435-447.
  • Azad, Reza. "Real-Time Illumination Invariant Face Detection Using Biologically Inspired Feature Set and BP Neural Network." International Journal of Information Engineering and Electronic Business (IJIEEB) 6.3 (2014): 9.
  • Azad, Reza, et al. "Real-Time Human-Computer Interaction Based on Face and Hand Gesture Recognition." arXiv preprint arXiv:1408.1549 (2014).
  • Hamedan, Iran. "Optimized Method for Real-Time Face Recognition System Based on PCA and Multiclass Support Vector Machine." (2013).
  • Fan, Jiancong. "OPE-HCA: an optimal probabilistic estimation approach for hierarchical clustering algorithm." Neural Computing and Applications: 1-11.
  • Hegde, Ganapatikrishna P., and M. Seetha. "REAL TIME VOTING SYSTEM USING FACE RECOGNITION FOR DIFFERENT EXPRESSIONS AND POSE VARIATIONS", International Journal of Research in Engineering and Technology, Vol 3, Issue 7, July 2014.