人脸遮挡区域检测与重建

Face Occlusion Detection and Reconstruction

提出一种基于模糊主分量分析技术(FPCA)的人脸遮挡检测与去除方法.首先,有遮挡人脸被投影到特征脸空间并通过特征脸的线性组合得到一个重建人脸.计算重建图与原图的差图像,加权滤波后并归一化作为被遮挡的概率,以此概率为权重由原图和重建图合成新的人脸.在后续迭代中,根据遮挡概率使用模糊主分量分析进行分析重建,并使用累积误差进行遮挡检测.实验结果表明,算法可精确定位人脸遮挡区域,得到平滑自然的重建人脸图像,优于经典的迭代PCA方法.

Face occlusions （such as glasses,respirator,scarf,etc.） can degrade the performance of face recognition and face animation evidently. How to remove occlusions on face image quickly and automatically becomes a important problem in face image processing. A face occlusion detection and reconstruction algorithm based on fuzzy principal component analysis （FPCA） is proposed in this paper. The main framework is based on analysis and synthesis techniques. In analysis step,optimal coefficients are estimate from occluded face by project to face space （eigenfaces） in the sense of least-square minimization （LSM）. In synthesis step,reconstructed face is obtained by linear combinations of prototypes. Then,difference image is computed from original and reconstructed face,and it is filtered by weights calculated from distance and gray level gap from current pixel to its surrounding neighbors,and normalized to [0 1]. This difference was used as the probability of being occluded. New face is synthesized by weighted sum of reconstructed face and original face based on this occluding probability. In succeeding iterations,fuzzy PCA in stead of classical PCA is used for analysis and reconstruction,and accumulated error is used for occlusion detection. Experimental results show that the proposed algorithm could detect face occlusion precisely and reconstruct smooth natural face,outperforming classical iterative PCA method.