目的 针对2维人脸难以克服光照、表情、姿态等复杂问题,提出了一种基于协作表示残差融合的新算法.方法 协作表示分类算法是将所有类的训练图像一起协作构成字典,通过正则化最小二乘法代替1范数求解稀疏系数,减小了计算的复杂度,由此系数重构测试人脸,根据重构误差最小原则,对测试人脸正确分类.该方法首先在3维人脸深度图上提取Gabor特征和Geodesic特征,然后在协作表示算法的基础上融合两者的残差信息,作为最终差异性度量,最后根据融合残差最小原则,进行人脸识别.结果 在不同的训练样本、特征维数条件下,在CIS和Texas 2 个人脸数据库上,本文算法的识别率可分别达到94.545%和99.286%.与Gabor-CRC算法相比,本文算法的识别率平均高出了10%左右.结论 在实时成像系统采集的人脸库和Texas 3维人脸库上的实验结果表明,该方法对有无姿态、表情、遮挡等变化问题具有较好的鲁棒性和有效性.

Objective To overcome the crucial problem of expression, illumination, and pose variations in 2D face recognition, a fused residual algorithm based on collaborative representation is proposed.Method Collaborative representation classification (CRC) algorithm combines all training images to constitute a dictionary collaboratively. CRC algorithm has less complexity by using regularized least square to solve sparse coefficients, and the coefficients reconstruct a testing face. Testing faces are classified correctly based on reconstruction residuals. This approach extracts Gabor and geodesic features from 3D face depth images, and then fuses two features via collaborative representation algorithm. The fused residuals serve as the ultimate difference metric. Finally, the minimum fused residual corresponds to the correct subject.Result While the Gabor feature has good scale and orientation selectivity, the geodesic feature has facial intrinsic geometric structure and robust to facial expression, CRC is insensitive to occlusion. Experiments are conducted on CIS and Texas face databases, and results show that the recognitions rates of the proposed method are up to 94.545% and 99.286%, respectively. The recognitions rates outperform that of Gabor-CRC by approximately 10%.Conclusion Comprehensive experiments on CIS and Texas database verify that the proposed algorithm is effective and robust.