Human Face Detection in Color Images using Fusion of Ada Boost and LBP Feature
الموضوعات : Majlesi Journal of Telecommunication Devices
Majid Emadi
1
,
Mehran Emadi
2
1 - Department of Electrical Engineering, Mobarakeh Branch, Islamic Azad Univesity, Mobarakeh, Isfahan, Iran
2 - Assistant Professor, Faculty of Electrical Engineering,Islamic Azad University, Mobarakeh Branch, Mobarakeh, Isfahan, Iran
الکلمات المفتاحية: Light challenge, Local binary pattern, face detection,
ملخص المقالة :
Face recognition has been one of the most widely used sub-disciplines of machine learning for so many years. Face detection has been employed as an effective method in a wide range of applications such as surveillance systems and Forensic pathology in the area of machine vision. However, the accuracy of face detection has dramatically declined over the past decade due to wide-ranging challenges such as face detection with changes in face angle, the density of the crowds in an image, quality of light, etc which require special attention of researchers in response to these challenges. In the present study, a new sustainable approach to light changes for face detection based on local features is employed. In this method, the local binary pattern is extracted from face images and Principal Component Analysis is utilized to reduce the feature vectors’ dimension by the descriptor. Eventually, the features are classified using Ada Boost. Tests done on the images on the web show that face recognition accuracy is 100% in the low density crowd, 96% in the high-density crowd and proper light conditions, and 90% in the high-density crowd and poor light conditions.
[1] Y. Ban, S. K. Kim, S. Kim, K.-A. Toh, and S. Lee, "Face detection based on skin color likelihood," Pattern Recognition, vol. 47, no. 4, pp. 1573-1585, 2014.
[2] H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, "Color image segmentation: advances and prospects," Pattern recognition, vol. 34, no. 12, pp. 2259-2281, 2001.
[3] R. Khan, A. Hanbury, J. Stöttinger, and A. Bais, "Color based skin classification," Pattern Recognition Letters, vol. 33, no. 2, pp. 157-163,2012.
[4] Z. Jin, Z. Lou, J. Yang, and Q. Sun, "Face detection using template matching and skin-color information," Neuro computing, vol. 70, no.6, pp. 794-800, 2007.
[5] S. A. Naji, R. Zainuddin, and H. A. Jalab, "Skin segmentation based on multi pixel color clustering models," Digital Signal Processing, vol. 22, no. 6, pp. 933-940, 2012.
[6] G. H. Joblove and D. Greenberg, "Color spaces for computer graphics," in ACM siggraph computer graphics: ACM, vol.12, no. 3, pp 20-25, 1987.
[7] Y. Jie, L. Xufeng, Z. Yitan, and Z. Zhonglong, "A face detection and recognition system in color image series," Mathematics and Computers in Simulation, vol. 77, no. 6, pp. 531-539, 2008.
[8] V. Vezhnevets, V. Sazonov, and A. Andreeva, "A survey on pixel-based skin color detection techniques," in Proc. Graphicon, vol. 3, pp. 85-92 Moscow, Russia, 2003.
[9] D. Pascale, "A review of rgb color spaces from xyy to r’g’b’," Babel Color, vol. 18, pp. 136-152, 2003.
[10] H. Yao and W. Gao, "Face detection and location based on skin chrominance and lip chrominance transformation from color images," Pattern recognition, vol. 34, no. 8, pp. 1555-1564, 2001.
[11] P. Kakumanu, S. Makrogiannis, and N. Bourbakis, "A survey of skin-color modeling and detection methods," Pattern recognition, vol. 40, no. 3, pp.1106-1122, 2007.
[12] J. M. Chaves-Gonzalez, M. A. Vega-Rodriguez, J. A. Gomez Pulido, and J. M. Sanchez-Perez, "Detecting skin in face recognition systems: A colour spaces study," Digital Signal Processing, vol. 20, no. 3, pp. 806-823, 2010.
[13] S. K. Singh, D. Chauhan, M. Vatsa, and R. Singh, "A robust skin color based face detection algorithm", Journal of science and Engineering, vol. 6 , no. 4. 2003.
[14] G. Zhang, X. Huang, S. Z. Li, Y. Wang, and X. Wu, "Boosting local binary pattern (LBP)-based face recognition," in Advances in biometric person authentication: Springer, pp. 179-186, 2004.
[15] K. B. Ge, J. Wen, and B. Fang, "Adaboost algorithm based on MB-LBP features with skin color segmentation for face detection," in Wavelet Analysis and Pattern Recognition (ICWAPR,2011) International Conference on, :IEEE, pp. 4043, 2011.
[16] Y. Wu and X. Ai, "Face detection in color images using AdaBoost algorithm based on skin color information," in Knowledge Discovery and Data Mining, WKDD 2008. First International Workshop on, IEEE:2008, pp. 339-338, 2008.
[17] V. Mutneja and S. Singh, "Modified Viola–Jones algorithm with GPU accelerated training and parallelized skin color filtering-based face detection," Journal of Real-Time Image Processing, pp. 1-21, 2017.
[18] S. Ma and L. Bai, "A face detection algorithm based on Adaboost and new Haar-Like feature," in Software Engineering and Service Science (ICSESS), 7th IEEE International Conference , pp, 651-654.
[19] P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," in Computer Vision and Pattern Recognition, CVPR 2001. Proceedings of the IEEE Computer Society Conference:IEEE, vol. 1, pp. 1-2, 2001.
[20] B. Wu, A. Haizhou, C. Huang, and S. Lao, "Fast rotation invariant multi-view face detection based on real adaboost", : IEEE in null, IEEE, p79, 2004.
[21] L. Sirovich and M. Meytlis, "Symmetry, probability, and recognition in face space," Proceedings of the National Academy of Sciences, vol. 106, no. 17pp. 6895-6999, 2009.
[22] A. Pentland, B. Moghaddam, and T. Starner, "View-based and modular eigenspaces for face recognition" 1994.
[23] Z. Chen, H. Liu, and Y. Wang, "Support Vector Machine-Based Face Direction Detection Using an Infrared Array Sensor," in Dynamics of Civil Structures: Springer, 2 ,vol. 2, pp309-317, 2019.
[24] W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, "Face recognition: A literature survey", ACM computing surveys (CSUR), vol. 35, no. 4, pp. 339-458, 2003.
[25] E. Hjelmås and B. K. Low, "Face detection: A survey," Computer vision and image understanding, vol. 83, no. 3, pp. 236-274-, 2001.
[26] T. Ojala, M. Pietikäinen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern recognition, vol. 29, no. 1, pp. 51-59,1996.
[27] Y. Freund, R. Schapire, and N. Abe, "A short introduction to boosting," Journal-Japanese Society For Artificial Intelligence, vol. 14, no.1612, pp.771- 780,1992.
