Principal Local Binary Patterns for Face Representation and Recognition

Abstract

Based on fitting the Local Binary Patterns (LBP) histogram into the bag-of-words paradigm, we propose an LBP variant termed Principal Local Binary Patterns (PLBP) which are learned in an unsupervised way from the data. The learning problem turns out to be the same as the Principal Component Analysis (PCA) and thus can be solved very efficiently. Unlike the manually specified patterns in LBP which distribute very non-uniformly, the learned patterns in PLBP can adapt with the distribution of the data so that they distribute very uniformly, which preserves more information than LBP in the binary coding process. Moreover, PLBP can take advantage of much larger neighborhood than LBP to describe the point, which provides more information. Therefore, PLBP contains more information than LBP to discriminate different classes. The experimental results of face recognition on the FERET and LFW datasets clearly confirm the discrimination power and robustness of PLBP. It achieves very competing performance on both datasets and it is very simple and efficient to compute.