In this paper, we propose a simple and efficient face representation feature that adopts the eigenfaces of Local Binary Pattern (LBP) space, referred to as the LBP eigenfaces, for robust face recognition. In the proposed method, LBP eigenfaces are generated by first mapping the original image space to the LBP space and then projecting the LBP space to the LBP eigenface subspace by Principal Component Analysis (PCA). Therefore, LBP eigenfaces capture both the local and global structures of face images. In the experiments, the proposed LBP eigenfaces are integrated into two types of classification methods, Nearest Neighbor (NN) and Collaborative Representation-based Classification (CRC). Experimental results indicate that the classification with the LBP eigenfaces outperforms that with the original eigenfaces and LBP histogram.

It has been over a decade since the "Eigenfaces" approach to automatic face recognition, and other appearance-based methods, made an impression on the computer vision research community and helped spur interest in vision systems being used to support biometrics and human-computer interface. In this paper I give a personal view of the original motivation for the work, some of the strengths and limitation of the approach, and progress in the years since. Appearance-based approaches to recognition complement feature- or shape-based approaches, and a practical face recognition system should have elements of both. Eigenfaces is not a general approach to recognition, but rather one tool out of many to be applied and evaluated in the appropriate context.