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. 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. 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%. Comprehensive experiments on CIS and Texas database verify that the proposed algorithm is effective and robust.