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Many state-of-the-art face recognition algorithms use image descriptors based on features known as Local Binary Patterns (LBPs). While many variations of LBP exist, so far none of them can automatically adapt to the training data. The authors introduce and analyze a novel generalization of LBP that learns the most discriminative LBP-like features for each facial region in a supervised manner. Since the proposed method is based on Decision Trees, they call it Decision Tree Local Binary Patterns or DT-LBPs. Tests on standard face recognition datasets show the superiority of DT-LBP with respect of several state-of-the-art feature descriptors regularly used in face recognition applications.
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