Collaborative Representation with regularized least square (CRC-RLS) is state-of-the-art face recognition method that exploits the role of collaboration between classes in representing the query sample. However, this method views the image as a point in a feature space, and the performance can be degraded when the cropped face image is misaligned and/or the lighting conditions change. Histogram-based features, such as Local Binary Patterns (LBP) have gained reputation as powerful and attractive texture descriptors showing excellent results in terms of accuracy in face recognition. In this paper, LBP features are introduced in CRC-RLS to confront these problems such as illumination. In addition, motivated by the recent success of non-linear approaches, a new kernel-based nonlinear regularized least square classifier with collaborative representation (KCRC-RLS) is proposed in this paper. The proposed system is evaluated on two benchmarks: ORL and Extended Yale B. The results indicate a significant increase in the performance when compared with state-of-the-art face recognition methods.