Approach Based on Contourlet Transform and Weighted Similarity Measure for Face Recognition
Contourlet transform (CT) is a new efficient image representation which identifies two key features of an image that improves over the separable 2-D wavelet transform, namely directionality and anisotropy. In this paper, a method based on contourlet transform and weighted similarity measure (WSM) for face recognition is proposed. Low frequency and the directional high frequency subbands coefficients can be produced by contourlet transformation on face images. For feature extraction, low-frequency coefficients are divided into a few sub-blocks. All of the means and standard deviations of each subblock constitute low frequency characteristic vectors. On the other hand, the histogram graphs of directional high frequency subband coefficients can be fitted with generalized Gaussian density (GCD) model. The similarity of low-frequency characteristic vectors is measured by Euclidean distance, and that of the high frequency components is measured by Kullback-Leibler (K-L) distance. The WSM is implemented by computing the weighted average of these two kinds of distances. The experimental results show that weighted similarity measure for contourlet-based face recognition can achieve higher recognition rates.
contourlet transform similarity Kullback-Leibler distance weighted distance face recognition
Lei Chen Jiajun Wang Bing Sun Xingrong Zhong Fei Shi
School of Electronics & Information Engineering Soochow University Suzhou,P R China,215021
国际会议
上海
英文
3033-3037
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）