Performance Improvement of the Traditional SVM-Based Face Detection Method
Subject Areas : electrical and computer engineeringM. Roohi 1 , G. Mirjalily 2 , M. T. Sadeghi 3
1 - Yazd University
2 - Yazd University
3 - Yazd University
Keywords: Face detectiondecision fusionsupport vector machine,
Abstract :
In this paper, we propose some ideas to improve the performance of the traditional face detection based on support vector machine (SVM). The traditional SVM-based system for face detection detects faces by exhaustively scanning an image for face-like patterns at any possible scales. It divides the original image into overlapping sub-images by using a fixed-size cutting window and classifies them using the Support Vector Machine to determine the appropriate class (face or non-face). This approach has not an acceptable detection rate. In this paper to improve the performance, we use cutting windows with different sizes. We fuse the decisions obtained by using different windows. An important issue in the Support Vector Machine classifier is to shift the decision threshold adequately towards the better represented class. In this paper, a novel method is proposed for determining the threshold value adaptively. A post processing algorithm is also presented for reducing the false alarm rate. Experimental results using standard database show that the performance of the proposed SVM-based method is much better than the basic SVM classifier.
[1] M. Yang, D. J. Kriegman, and N. Ahuja, "Detecting faces in images: a survey," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 34-58, Jan. 2002.
[2] C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121-158, Jun. 1998.
[3] E. Osuna, R. Freund, and F. Girosi, "Support Vector Machines: Training and Applications," A. I. Memo, No. 1602, MIT A. I. Lab., 1997.
[4] E. Osuna, R. Freund, and F. Girosi, "Training support vector machines: an application to face detection," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 130-136, Jun. 1997.
[5] N. Cristianini and J. Shawe - Taylor, An Introduction to Support Vector Machines, Cambridge University Press, 2000.
[6] B. Murtagh and M. Saunders, "Large-scale linearly constrained optimization," Mathematical Programming, vol. 14, no. 1, pp. 41-72, Dec. 1978.
[7] http://SVMlight.joachims.org.
[8] C. Shavers, R. Li, and G. Lebby, "An SVM-based approach to face detection," in Proc. of 38th Southeastern Symp. on System Theory, vol. 1, pp. 362-366, US, Mar. 2006.
[9] Q. Gao, Q. Pan, H. Zhang, Y. Cheng, and Q. Tlan, "Face detection using SVM trained in independent space," in Proc. of the Third Int. Conf. on Machine Learning and Cybernetics, vol. 6, pp. 3674-3677, Shanghai, Aug. 2004.
[10] P. Shih and C. Liu, "Face detection using discriminating feature analysis and support vector machine," Elsevier Pattern Recognition J., vol. 39, no. 2, pp. 260-276, Feb. 2006.
[11] A. Wring and X. Liu, "Face detection using spectral histograms and SVMs," IEEE Trans. Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 35, no. 3, pp. 467-476, Jun. 2005.
[12] B. Heisele, T. Serre, S. Prentice, and T. Poggiob, "Hierarchical classification and feature reduction for fast face detection with support vector machines," Pattern Recognition, vol. 36, no. 9, pp. 2007-2017, Sep. 2003.
[13] H. Sahbi, Coarse - to - Fine Support Vector Machines for Hierarchical Face Detection, Ph.D. Thesis, Versailles University, France, 2003.
[14] M. Roohi, G. Mirjalily, and M. T. Sadeghi, "Face detection using a modified SVM - based classifier," in Proc. of IEEE Int. Conf. on Computational Intelligence and Multimedia Applications, ICCIMA, vol. 2, pp. 354-360, Dec. 2007.
[15] G. Mirjalily, M. Roohi, and M. T. Sadeghi, "A new approach for face detection using support vector machine," in Proc. of First Joint Cogress on Fuzzy and Intelligent Systems, Ferdowsi University of Mashad, vol. 1, pp. 435-440, Aug.. 2007.
[16] M. Roohi, Face Detection Using Support Vector Machine, M.Sc. Thesis, Yazd University, 2007.
[17] http://www.vasc.ri.cmu.edu/idb/html/face/frontal_images/index.html
