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. We 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, we 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. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Maturana, D., Mery, D., & Soto, Á. (2011). Face recognition with decision tree-based local binary patterns. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6495 LNCS, pp. 618–629). https://doi.org/10.1007/978-3-642-19282-1_49
Mendeley helps you to discover research relevant for your work.