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Face recognition with local binary patterns
| Venue: | In Proc. of 9th Euro15 We |
| Citations: | 302 - 15 self |
BibTeX
@INPROCEEDINGS{Ahonen_facerecognition,
author = {Timo Ahonen and Abdenour Hadid and Matti Pietikäinen},
title = {Face recognition with local binary patterns},
booktitle = {In Proc. of 9th Euro15 We},
year = {},
pages = {469--481}
}
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Abstract
Abstract. In this work, we present a novel approach to face recognition which considers both shape and texture information to represent face images. The face area is first divided into small regions from which Local Binary Pattern (LBP) histograms are extracted and concatenated into a single, spatially enhanced feature histogram efficiently representing the face image. The recognition is performed using a nearest neighbour classifier in the computed feature space with Chi square as a dissimilarity measure. Extensive experiments clearly show the superiority of the proposed scheme over all considered methods (PCA, Bayesian Intra/extrapersonal Classifier and Elastic Bunch Graph Matching) on FERET tests which include testing the robustness of the method against different facial expressions, lighting and aging of the subjects. In addition to its efficiency, the simplicity of the proposed method allows for very fast feature extraction. 1
Keyphrases
face recognition local binary pattern face image dissimilarity measure face area elastic bunch graph matching texture information extensive experiment feret test bayesian intra extrapersonal classifier enhanced feature histogram small region novel approach different facial expression computed feature space fast feature extraction chi square neighbour classifier
