An improved detection algorithm of face with combining adaboost and SVM

This paper proposes a face detection method by combining SVM into the traditional face detection algorithm named AdaBoost. This method adopts SVM classifier instead of traditional AdaBoost classifier based on AdaBoost cascade framework. To get SVM classifier, extract several Haar-like features with the strongest classification ability in the process of training SVM classifier. To ensure the generalization capability of classifier classify validation set, and using the penalty factor setting with focus makes SVM classifier pay more attention to the face samples, while the training samples reached a high goodness of fit. At the same time, this thesis improves the extraction method of Haar-like feature. Finally, using Matlab simulation test the effectiveness of the algorithm. The experiments show that the improved detection algorithm of face with combining AdaBoost and SVM reduces the amount of computation and improve the detection rate.

[1]  Goutam Sanyal,et al.  An efficient face recognition approach using PCA and minimum distance classifier , 2011, 2011 International Conference on Image Information Processing.

[2]  Xiaoou Tang,et al.  Dual-space linear discriminant analysis for face recognition , 2004, CVPR 2004.

[3]  Li Yingxin and Ruan Xiaogang,et al.  Feature Selection for Cancer Classification Based on Support Vector Machine , 2005 .

[4]  J. R. Perkins,et al.  The time interval alignment (IA) policies, boundary and applications with multiple stream arrivals , 2011 .

[5]  Tiancang Du,et al.  Improved Adaboost Face Detection , 2010, 2010 International Conference on Measuring Technology and Mechatronics Automation.

[6]  Hu Yan,et al.  Remote sensing image classification based on SVM classifier , 2011, 2011 International Conference on System science, Engineering design and Manufacturing informatization.

[7]  Jiu-long Zhang,et al.  Face recognition based on curvelet and PCA: Face recognition based on curvelet and PCA , 2008 .

[8]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[10]  Zhang Li-wen,et al.  Appropriateness in Applying SVMs to Text Classification , 2010 .

[11]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Wang Ning.  Prediction of Shallow-water Reverberation Time Series Using Support Vector Machine , 2008 .

[13]  Udayan Baruah,et al.  Face Recognition using Support Vector Machine , 2014 .

[14]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[15]  Ja-Chen Lin,et al.  A new LDA-based face recognition system which can solve the small sample size problem , 1998, Pattern Recognit..

[16]  Lin Kaibiao,et al.  The Method of Fax Receiver's Name Recognition Based on SVM , 2006 .

[17]  Xiaogang Wang,et al.  Dual-space linear discriminant analysis for face recognition , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..