COMBINATION OF DUAL-TREE COMPLEX WAVELET AND SVM FOR FACE RECOGNITION
Based on the attractive property such as shift invariance, good directional selectivity, limited redundancy and efficient computation of dual-tree complex wavelet transform, a novel face recognition method with combining of dual-tree complex wavelet transform and support vector machine is proposed in this paper. Firstly, it uses 2-D dual-tree complex wavelet transform to decompose each face image into six band-pass sub-images that are strongly oriented at 6 different angles and two low-pass sub-images and extracts the human face features. Then principal component analysis technique is used to reduce the feature dimensions. Finally, support vector machine is used as classifier. Through the comparative experiments between the Gabor wavelet approach and the 2-D dual-tree complex wavelet transform approach, the results show that the proposed approach can achieve higher recognition rate no matter what SVM kernel is used. Also, experiments show that the proposed method needs least computation time.
Dual-tree complez wavelet transform SVM Principal component analysis Face recognition
GUO-YUN ZHANG SHI-YU PENG HONG-MIN LI
Dept.of Physics and Electronics Information, Hunan Institute of Science and Technology, Yueyang 4140 College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
2815-2819
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）