基于图像重构和l_0范数稀疏表示的人脸识别算法

Face Recognition Based on Image Reconstruction and l_0 Norm Sparse Representation Classification

用现有的人脸识别方法处理人脸姿态和光照的变化仍有一定的难度,本文提出一种基于图像重构和l_0范数稀疏表示的人脸识别算法:首先,采用深度学习网络提取人脸特征;然后,根据提取的特征重构人脸图像;最后,用l_0范数快速稀疏分类的识别算法在重构图像上进行识别.基于FERET人脸数据库的实验结果表明,本算法可在姿态变化比较大的情况下保持较高的人脸识别率以及较快的识别速度.

It is still difficult to use the existing face recognition methods to deal with the changes in face attitude and illumination. This paper proposes a face recognition algorithm based on image reconstruction and l0 norm sparse representation classification. First, face features are extracted by the depth learning network; then, the face image is reconstructed based on the feature extracted; and finally, the recognition algorithm based on the fast sparse classification algorithm is used to identify the reconstructed images. Experimental results based on FERET face database demonstrate that the proposed algorithm can greatly improve recognition rate and speed under various conditions such as large attitude change.