Face Recognition Based on Second Generation of Curvelet Transform and Kernal Principal Component Analysis

摘要：

A new face recognition method is proposed by adopting the Second Generation of Curvelet Transform (SGCT) and Kernel Principal Component Analysis (KPCA). Based on KPCA, the face recognition algorithm can extract nonlinear image features and show better performance under the conditions of small sample training. However, the disadvantage of KPCA is the image information redundancy, which reduces the recognition performance. Traditional wavelet transform method of preprocessing removes irrelevant details of identification, but in high-dimensional image signal, the wavelet analysis is not the optimal method. In this paper, the new multi-scale geometric analysis, SGCT, is proposed to preprocess the image in order to reduce the high dimensional operators and improve accuracy of KPCA. Based on ORL database, experimental results show that the proposed method has a faster recognition speed and higher recognition accuracy than the traditional methods.

关键词： Face Recognition Second Generation of Curvelet Transform (SGCT) Kernel Principal Component Analysis (KPCA)

作者: Peipei Shi Xuebin Li

作者单位: Department of Information Science and Technology Beijing University of Chemical Technology Beijing, China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 1534-1537

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）