基于异值区域消除的遮挡人脸识别

Face Recognition with Occlusion Based on Removing Outliers Area

针对存在部分遮挡的人脸的识别问题,提出了一种改进的基于异值区域消除的人脸识别方法。首先,由训练人脸图像得到平均脸图像,并将测试图像与平均脸图像作差值运算得到误差人脸图像;然后,对误差人脸图像进行分割得到测试人脸图像存在的遮挡区域,并将测试图像和训练图像的相应区域予以去除以形成新的测试图像和训练图像;最后,采用线性回归分类或稀疏编码分类方法实现人脸识别。与同类方法比较,本方法计算相对简单,展现了较好的识别性能提升。基于Yale B和AR标准人脸数据库的仿真测试结果验证了本方法的有效性。

Aiming at the issue of face recognition with partial occlusion,an improved face recognition method based on removing the outlier area was proposed in this paper.A mean face image is firstly obtained from train images,which is subtracted by the test face to form an error face image.Then the error face image is used to obtain the occlusion area of the test image by image segmentation technique,and the train images and test image are tailored by removing the corresponding occlusion area.Finally,face recognition is performed by linear regression classifier or sparse coding classifier.Compared to the similar works,the proposed method has considerable recognition performance improvement with relatively sample computational complexity.Simulation results based on the standard extended Yale B and AR face databases show effectiveness of the proposed method.