Multi-scale local binary pattern histograms for face recognition

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Abstract

A novel discriminative face representation derived by the Linear Discriminant Analysis (LDA) of multi-scale local binary pattern histograms is proposed for face recognition. The face image is first partitioned into several non-overlapping regions. In each region, multi-scale local binary uniform pattern histograms1 are extracted and concatenated into a regional feature. The features are then projected on the LDA space to be used as a discriminative facial descriptor. The method is implemented and tested in face identification on the standard Feret database and in face verification on the XM2VTS database with very promising results. © Springer-Verlag Berlin Heidelberg 2007.

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Chan, C. H., Kittler, J., & Messer, K. (2007). Multi-scale local binary pattern histograms for face recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4642 LNCS, pp. 809–818). Springer Verlag. https://doi.org/10.1007/978-3-540-74549-5_85

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