Determining Effective Features for Face Detection Using a Hybrid Feature Approach

Authors

1 Electrical Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran

2 Electrical Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran.

Abstract

Detecting faces in cluttered backgrounds and real world has remained as an unsolved problem yet. In this paper, by using composition of some kind of independent features and one of the most common appearance based approaches, and multilayered perceptron (MLP) neural networks, not only some questions have been answered, but also the designed system achieved better performance rather than the previously presented works. The designed face detection algorithm is composed of two main stages; in the first stage of the algorithm, color based skin detection is performed using some selected color space components from the whole 15 color space components in order to reduce the search space and in the second one, verification of detected regions is done using some other kinds of different features including texture, gradient, image and geometric features. Unlike the other studied issues, in this paper, various types of features aren't evaluated in separated algorithms and systems; rather they compete all together in one competitive learning vector and after the training of the neural network the system participates in the process of feature selection. Using designed method and besides of dimensional reduction of input matrix extraordinarily, each chosen feature was ranked.

Keywords


[1] M. Bertozzi, A. Broggi, M.Del Rose, M Felisa, A. Rakotomamonjy and F. Suard, “A Pedestrian Detector Using Histograms of Oriented Gradients and a Support Vector Machine Classifier”, In Proc. IEEE Intelligent Transportation Systems Conference, pp.143-148, 2007.
[2] P. Kakumanu, S. Makrogiannis, and N. Bourbakis, “A
Survey of Skin-Color Modeling and Detection Methods”, Elsevier Pattern Recognition., Vol.40, No.3, pp.1106 – 1122, 2007.
[3] Wen-Hsiang Lai, Chang-Tsun Li, “Skin Colour Based Face Detection in Color Images”, IEEE International Conference on AVSS06, IEEE Computer Society, pp.56-61, 2006.
[4] Chang Huang, Haizhou Ai, Yuan Li, and Shihong Lao, “High-Performance Rotation Invariant Multiview Face Detection”, IEEE Trans. on Pattern Analysis and machine intelligence ACTIONS, Vol.29, No.4, pp.671-686, 2007.
[5] Lun Zhang, Rufeng Chu, Shiming Xiang, Shengeai Liao,and Stan Z. Li, “Face Detection Based on Multi-Block LBP Representation”, Center for Biometrics and Security Research & Natuinal Laboratory of Pattern Recognition, China, 2007.
[6] M. Nilson, J. Nordberg, and I. Claesson, “Face Detection Using Local SMQT Features and Split up snow Classifier”, IEEE ICASSP’07, Vol.2, pp.589-592., 2007.
[7] Ming-Hsuan Yang, David J. Kriegman, and Narendra Ahuja, “Detecting Faces in Images: A Survey”, IEEE Trans. On Pattern Analysis and Machine Intelligence., Vol.24, No.1, pp.34-58, 2002.
[8] L.G. Valiant, “A Theory of the Learnable”, Communications of the ACM, Vol.27, No.11, pp.1134–1142, 1984.
[9] S. Montabone, and A. Soto, “Human Detection Using a Mobile Platform and Novel Features Derived from a Visual Saliency Mechanism”, Image and Vision Computing., Vol.28, No.3, pp.391-402, 2010.
[10] Sanjay Kr. Singh, D.S. Chauhan, Mayank Vatsa, Richa Singh, “A Robust Skin Color Based Face Detection
Algorithm, Tamkang Journal of Science and Engineering, Vol.6, No.4, pp.227-234, 2003.
[11] Paul Viola, Michael Jones, “Robust Real-Time Object Detection”, International Journal of Computer Vision (IJCV), Vol.2, pp.137–154, 2004.
[12] S.Z. Li, ZhenQiu Zhang, “Float boost Learning and Statistical Face Detection”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.26, No.9, pp.1112–1123, 2004.
[13] S. Araban, F. Farokhi, K. Kangarloo, “Determining Effective Colour Components for Skin Detection Using a Clustered Neural Network”, IEEE International Conference Signal and Image Processing Applications (ICSIPA11), pp.541-546, 2011.
[14] L. Zhang and R. Nevatia, “Efficient Scan-Window Based Object Detection Using GPGPU”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops (CVPRW08), pp.1-7, 2008.
[15] N. Dalal and B.l Triggs, “Histograms of Oriented Gradients for Human Detection”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops (CVPR05), Vol.1, pp.886-893, 2005.
[16] H. Zhang, W. Gao, Xilin Chen, Debin Zhao, “Object Detection Using Spatial Histogram Features”, Image and Vision Computing, Vol.24, No.4, pp.327–341, 2006.
[17] L. Itti, “The ilab Neuromorphic Vision C++ Toolkit: Free Tools for the Next Generation of Vision Algorithms”, The Neuromorphic Engineer, Vol.1, No.1, 2004.
[18] S. Frintrop, “VOCUS: A Visual Attention System for Object Detection and Goal directed Search”, PhD thesis, Rheinische Friedrich-Wilhelms University, Bonn Germany, 2005.
[19] R. C. Gonzalez, R. E. Woods, “Digital Image Processing”, Prentice Hall Edition, 2008.
[20] M. Pietika¨inen, T. Ojala, Z. Xu, “Rotation-invariant Texture Classification Using Feature Distributions”, Pattern Recognition Vol.33, pp.43–52, 2000.
[21] T. Ojala, M. Pietika¨inen, T. Ma¨enpa¨, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.24, No.7, pp.971–987, 2002.
[22] L. Itti, C. Koch