基于对称Gabor特征和稀疏表示的人脸识别

受启发于人脸近似对称的先验知识，提出一种基于对称Gabor特征的稀疏表示算法并成功运用于人脸识别。首先把人脸图像进行镜像变换得到其镜像图像，进而将人脸分解为奇偶对称脸。在奇偶对称脸上分别提取Gabor特征，得到Gabor奇偶对称特征。通过一个加权因子，将奇偶特征融合生成新的特征。最后用这种新的特征构成超完备字典进行稀疏表示人脸分类。在人脸数据库AR和FERET上的实验结果表明所提算法在人脸有表情、姿势和光照变化情况下仍能获得较高的识别率。

Face recognition based on symmetric Gabor features and sparse representation

Inspired by prior knowledge of face images' approximate symmetry, an algorithm based on symmetric Gabor features and sparse representation was proposed, which was successfully applied into face recognition in the paper. At first, mirror transform was performed on face images to get their mirror images, with which the face images could be decomposed into odd-even symmetric faces. Then, Gabor features were extracted from both odd faces and even faces to get the Gabor odd-even symmetric features，which could be fused via a weighting factor to generate the new features. At last, the newly obtained features were combined to form an over-complete dictionary which was used by sparse representation to classify the faces. The experimental results on AR and FERET face databases show that the new method can achieve high accuracy even when face images are under expression, pose and illumination variations.