非采样Contourlet变换与局部二值模式相结合的人脸识别

Face recognition using nonsubsampled Contourlet transform and local binary pattern

针对人脸识别中姿态、光照和表情变化带来的识别率有限的问题,提出了一种基于非采样Contourlet变换(NSCT)与局部二值模式(LBP)的人脸特征提取方法。首先对人脸图像进行非采样Contourlet变换,得到多尺度、多方向的子带系数矩阵,然后利用LBP算子从每个子带系数矩阵上抽取局部邻域关系,得到各子带的LBP特征图谱,最后将这些图谱分块统计并级联后作为人脸的识别特征。利用多通道最近邻分类器的分类结果表明,所提方法能有效提高识别率,所提取的特征对光照、表情和姿态等变化具有更好的鲁棒性。

Concerning the problem of limited recognition rate caused by variations in position,illumination and expression in face recognition,an efficient face recognition method based on Nonsubsampled Contourlet Transform（NSCT） and Local Binary Pattern（LBP） was proposed.Firstly,a face image was decomposed with NSCT,and NSCT coefficients in different scales and various orientations were obtained;LBP operator was then used to get LBP feature maps by extracting local neighboring relationship from NSCT coefficients;Finally,feature maps were respectively divided into several blocks,the concatenated histograms,which were calculated over each block,were used as the face features.The experimental results using multi-channel nearest neighbor classifier based on Euclidean distance show that,the proposed method can improve the recognition rate effectively,and the extracted feature is robust to variations of illumination,face expression and position.