Face Description with Local Binary Patterns Application to Face Recognition.pdf

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Face Description with Local Binary Patterns Application to Face Recognition.pdf

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Face Description with Local Binary Patterns Application to Face Recognition

1Face Description with Local Binary Patterns: Application to Face Recognition Timo Ahonen, Student Member, IEEE, Abdenour Hadid, and Matti Pietika?inen, Senior Member, IEEE Abstract This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed. Index Terms Facial image representation, local binary pattern, component-based face recogni- tion, texture features, face misalignment I. INTRODUCTION Automatic face analysis which includes, e.g., face detection, face recognition and facial expression recognition has become a very active topic in computer vision research [1]. A key issue in face analysis is finding efficient descriptors for face appearance. Different holistic methods such as Principal Component Analysis (PCA) [2], Linear Discriminant Analysis (LDA) [3] and the more recent 2-D PCA [4] have been studied widely but lately also local descriptors T. Ahonen, A. Hadid, and M. Pietika?inen are with the Machine Vision Group, Infotech Oulu, Department of Electrical and Information Engineering, University of Oulu, PO Box 4500, FIN-90014, Finland. E-mail: ftahonen, hadid, mkpg@ee.oulu.fi. 5th June 2006 DRAFT 2have gained attention due to their robustness to challenges such as pose and illumination changes. This paper presents a novel descriptor based on local binary pattern texture features extracted from local facial regions. One of the first face descriptors based on information extracted from local regions is the eigenfeatures method proposed by Pentland et al. [5]. This is a hybrid approach in which the features are obtained by performing PCA to local face