Local Variation Joint Representation for Face Recognition with Single Sample per Person
Sparse representation based classification (SRC) was originally applied to multiple-training-sample face recognition with promising performance. Recently SRC has been extended to face recognition with single sample per person by using variations extracted
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Abstract. Sparse representation based classification (SRC) was originally applied to multiple-training-sample face recognition with promising performance. Recently SRC has been extended to face recognition with single sample per person by using variations extracted from a generic training set as an additional common dictionary. However, the extended SRC ignored to learn a better variation dictionary and to use local region information of face images. To address this issue, we propose a local variation joint representation (LVJR) method, which learns a variation dictionary and does joint and local collaborative representation for a query image. The learned variation dictionary was required to do similar representation for the same-type facial variations, while the joint and local collaborative representation could effectively use local information of face images. Experiments on the large-scale CMU Multi-PIE and AR databases demonstrate that the proposed LVJR method achieves better results compared with the existing solutions to the single sample per person problem. Keywords: Local variation · Joint representation · Face recognition · Single sample per person
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Introduction
As one of the most visible applications in computer vision and pattern recognition, face recognition (FR) has been receiving significant attention in the community [17]. In practical FR scenarios such as face identification/verification in uncontrolled or less controlled environment [6, 16], there are many problems which have attracted much attention of researchers. For instance, face recognition with single sample per person is one of the most important FR problems. In the scenarios (e.g., law enforcement, e-passport, driver license, etc), there is only a single training face image per person. This makes the problem of FR particularly hard since very limited information is provided to predict the variations in the query sample. How to achieve high FR performance in the case of single training sample per person (SSPP) is an important and challenging problems in FR. The performance of FR would be greatly affected by the limited number of training samples per person [26]. First, many discriminant subspace and manifold learning algorithms (e.g., LDA and its variants [15]) cannot be directly applied to FR with SSPP. © Springer-Verlag Berlin Heidelberg 2015 H. Zha et al. (Eds.): CCCV 2015, Part II, CCIS 547, pp. 41–50, 2015. DOI: 10.1007/978-3-662-48570-5_5
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Second, sparse representation based classification (SRC) [12], cannot be easily applied to the problem of SSPP, either, since multiple training samples per person are needed to well reconstruct the query face. As reviewed in [26], many specially designed FR methods have been developed. According to the availability of an additional generic training set, the FR methods for SSPP can be divided into two categories: methods without using a generic training set, and methods with generic learning. The SSPP methods without generic learning often extract robust local features (e.g., g
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