基于NSCT和M-PCNN的人脸特征提取  

Facial Feature Extraction Based on NSCT and M-PCNN

作　　者：杨光[1] 王晅[1] 徐鹏[1] 陈丹丹[1] 

机构地区：[1]陕西师范大学物理与信息技术学院,西安710062

出　　处：《计算机工程》2012年第22期151-153,158,共4页Computer Engineering

基　　金：陕西省自然科学基础研究计划基金资助项目(2009JM8003)

摘　　要：为提高人脸识别对人脸姿态、位置、表情变化的鲁棒性,提出一种基于非下采样Contourlet变换(NSCT)与改进脉冲耦合神经网络(M-PCNN)的人脸特征提取方法。利用NSCT对输入图像进行多尺度分解和多方向稀疏分解,以捕获图像中的高维奇异信息,使用M-PCNN模型提取各子带的信息熵,将其作为人脸特征,利用支持向量机(SVM)实现分类与识别。仿真结果表明,该方法鲁棒性较强,在识别和分类中表现出较好的性能。In order to improve the robustness of face recognition to the changes of facial pose, position and expression, a facial feature extraction method based on Nonsubsampled Contourtlet Transform（NSCT） and Modified Pulse-coupled Neural Network（M-PCNN） is proposed in this paper. By using NSCT, the input images are decomposed into a number of sub-images with various scales and directional features. The different subbands are decomposed into a sequence of binary images by using M-PCNN. The information entropies of each binary images are calculated and regarded as facial features. A Support Vector Machine（SVM） classifier is employed to implement recognition and classification. Simulation results show that this method has good robustness, and can achieve better result in verification and classification.

关 键 词：人脸识别 主成分分析 保局投影 特征提取 信息熵 支持向量机 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]