To overcome the problem of illumination, facial expression and pose variants in 3D face recognition, the algorithm based on the Gabor features combined with kernel collaborative representation is proposed. Collaborative representation classification algorithm uses similar faces to describe testing faces collaboratively. Via solving sparse coefficient by l2 norm, collaborative representation classification algorithm can classify the testing face correctly according to reconstruction error. This method extracts 40 Gabor features with different scales and orientations of depth maps by the Gabor filter firstly. Secondly, these features are mapped to a highdimensional space by selecting an appropriate kernel function. Finally, nonlinear dimensionality reduction and feature selection fulfill the recognition task in highdimensional space combining with collaborative representation classification. Extensive experiments on Kinect Face Dataset and Texas 3D face database demonstrate that the proposed algorithm is more effective than recently algorithms even when the number of training samples is small.