Expression-robust 3D face recognition via weighted sparse representation of multi-scale and multi-component local normal patterns
KAUST DepartmentVisual Computing Center (VCC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
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AbstractIn the theory of differential geometry, surface normal, as a first order surface differential quantity, determines the orientation of a surface at each point and contains informative local surface shape information. To fully exploit this kind of information for 3D face recognition (FR), this paper proposes a novel highly discriminative facial shape descriptor, namely multi-scale and multi-component local normal patterns (MSMC-LNP). Given a normalized facial range image, three components of normal vectors are first estimated, leading to three normal component images. Then, each normal component image is encoded locally to local normal patterns (LNP) on different scales. To utilize spatial information of facial shape, each normal component image is divided into several patches, and their LNP histograms are computed and concatenated according to the facial configuration. Finally, each original facial surface is represented by a set of LNP histograms including both global and local cues. Moreover, to make the proposed solution robust to the variations of facial expressions, we propose to learn the weight of each local patch on a given encoding scale and normal component image. Based on the learned weights and the weighted LNP histograms, we formulate a weighted sparse representation-based classifier (W-SRC). In contrast to the overwhelming majority of 3D FR approaches which were only benchmarked on the FRGC v2.0 database, we carried out extensive experiments on the FRGC v2.0, Bosphorus, BU-3DFE and 3D-TEC databases, thus including 3D face data captured in different scenarios through various sensors and depicting in particular different challenges with respect to facial expressions. The experimental results show that the proposed approach consistently achieves competitive rank-one recognition rates on these databases despite their heterogeneous nature, and thereby demonstrates its effectiveness and its generalizability. © 2014 Elsevier B.V.
SponsorsThis work was supported in part by the French research agency, Agence Nationale de Recherche (ANR), under Grant ANR 2010 INTB 0301 01; the National Natural Science Foundation of China (NSFC) under Grant 61061130560 and Grant 61202237; the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20121102120016); the research program of State Key Laboratory of Software Development Environment (SKLSDE-2013ZX-31); the joint project by the LIA 2MCSI lab between the group of Ecoles Centrales and Beihang University; and the Fundamental Research Funds for the Central Universities.