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ANN Face Detection with Skin Color Distribution Rules

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Języki publikacji
EN
Abstrakty
EN
This paper develops a face detection method in color images using a multi-layer Neural Network classification. The proposed method is based on two image processing steps which first detect skin regions in the color image and then extract face information from those regions. Instead of performing huge search in every part of the test images, a pre-processing method for candidate face regions guides the image search using neural networks. The new algorithms perform fast and accurate face detection. Experiments have been carried out and satisfactory results have been obtained which indicate the robustness of the first process to detect faces under different environmental conditions.
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Twórcy
  • Minia University, Faculty of Engineering, Dept. of Electrical, Communications and Electronics section, Egypt
Bibliografia
  • 1. M. Riedmiller and H. Braun, "A direct adaptive method for faster back propagation learning: The prop algorithm," in IEEE International Conference on Neural Networks, vol. 1, pp. 586-591, 1993.
  • 2. M. Hagan and M. Menhaj, "Training feedforward networks with the marquardt algorithm," IEEE Transactions on Neural Networks, vol. 5, pp. 989-993, 1994.
  • 3. T. K. Leung, M. C. Burl, P. Perona, Finding faces in cluttered scenes using random labeled graph matching, Proceedings of the Fifth International Conference on Computer Vision, p.637, June 20-23, 1995.
  • 4. E. Hjelms and B-K. Low, "Face Detection: A Survey", Computer Vision and Image Understanding, vol. 83, pp. 236-274, 2001.
  • 5. Ming-Hsuan Yang, David J. Kriegman, Narendra Ahuja, Detecting Faces in Images: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, v. 24 n. 1, pp. 34-58, January 2002.
  • 6. L. Ma, K. Khorasani, Facial expression recognition using constructive feedforward neural networks, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, v. 34 n. 3, pp. 1588-1595, June 2004.
  • 7. Silvia Ferrando, Gianluca Gera, Carlo Regazzoni, Classification of Unattended and Stolen Objects in Video-Surveillance System, Proceedings of the IEEE International Conference on Video and Signal Based Surveillance, pp. 21, November 22-24, 2006.
  • 8. P. Kakumanu, S. Makrogiannis, N. Bourbakis, A survey of skin-color modeling and detection methods, Pattern Recognition, v. 40 n. 3, pp. 1106-1122, March, 2007.
  • 9. Thou-Ho (Chao-Ho) Chen, Yu-Feng Lin, Tsong-Yi Chen, Intelligent Vehicle Counting Method Based on Blob Analysis in Traffic Surveillance, Proceedings of the Second International Conference on Innovative Computing, Informatio and Control, pp. 238, September 05-07, 2007.
  • 10. Hsiuao-Ying Chen, Chung-Lin Huang, Chih-Ming Fu, Hybrid-boost learning for multi-pose face detection and facial expression recognition, Pattern Recognition, v. 41 n. 3, pp. 1173-1185, March, 2008.
  • 11. Yanwen Wu, Xueyi Ai, Face Detection in Color Images Using AdaBoost Algorithm Based on Skin Color Information, Proceedings of the First International Workshop on Knowledge Discovery and Data Mining, pp. 339-342, January 23-24, 2008.
  • 12. T. Y. Chen, T. H. Chen, and D. J. Wang, A cost-effective people-counter for passing through a gate based on image processing, International Journal of Innovative Computing, Information and Control, vol. 5, no. 3, pp. 785-800, 2009.
  • 13. Jun-Bao Li, Jeng-Shyang Pan, Zhe-Ming Lu, Kernel optimization-based discriminant analysis for face recognition, Neural Computing and Applications, v. 18 n. 6, pp. 603-612, September 2009.
  • 14. Jun-Bao Li , Jeng-Shyang Pan , Zhe-Ming Lu, Face recognition using Gabor-based complete Kernel Fisher Discriminant analysis with fractional power polynomial models, Neural Computing and Applications, v.18 n.6, p.613-621, September 2009.
  • 15. Cheng-Chang Lien, Ya-Lin Huang, Chin-Chuan Han, People Counting Using Multi-Mode Multi-Target Tracking Scheme, Proceedings of the 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 1018-1021, September 12-14, 2009.
  • 16. Chris Poppe, Sarah De Bruyne, Tom Paridaens, Peter Lambert, Rik Van de Walle, Moving object detection in the H.264/AVC compressed domain for video surveillance applications, Journal of Visual Communication and Image Representation, v. 20 n. 6, pp. 428-437, August, 2009.
  • 17. B. Sugandi, H. Kim, J. K. Tan, and S. Ishikawa, Real time tracking and identification of moving persons by using a camera in outdoor environment, International Journal of Innovative Computing, Information and Control, vol. 5, no. 5, pp. 1179-1188, 2009.
  • 18. Wenlong Zheng, Suchendra M. Bhandarkar, Face detection and tracking using a Boosted Adaptive Particle Filter, Journal of Visual Communication and Image Representation, v. 20 n. 1, pp. 9-27, January, 2009.
  • 19. W. C. Hu and C. Y. Yang, An improved full search algorithm with adaptive template block for fast and accurate object tracking, International Journal of Innovative Computing, Information and Control, vol. 6, no. 11, pp. 5115-5130, 2010.
  • 20. S. Krinidis, and I. Pitas, Statistical analysis of human facial expressions, Journal of Information Hiding and Multimedia Signal Processing, vol. 1, no. 3, pp. 241-260, 2010.
  • 21. R. Nishimura, S. Abe, N. Fujita, and Y. Suzuki, Reinforcement of VoIP security with multipath routing and secret sharing scheme, Journal of Information Hiding and Multimedia Signal Processing, vol. 1, no. 3, pp. 204-219, 2010.
  • 22. H. Sayoud, and S. Ouamour, Proposal of a new confidence parameter estimating the number of speakers-an experimental investigation, Journal of Information Hiding and Multimedia Signal Processing, vol. 1, no. 2, pp. 101-109, 2010.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWA0-0052-0010
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