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Wenjuan Gong 1 and Weishan Zhang 1 and Jordi Gonzalez 2 and Yan Ren 1 and Zhen Li 3
Academic Editor:Houbing Song
1, China University of Petroleum, Qingdao 266580, China
2, Computer Vision Center, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain
3, Sichuan Water Conservancy Vocational College, Chongzhou, Sichuan 611231, China
Received 30 November 2014; Revised 7 February 2015; Accepted 8 February 2015; 30 August 2015
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction
Face recognition has been an active research area due to its applications in security entry systems and automatic law enforcement system. Pose variances, illumination changes, and noise from observations make it hard to define a model with promising recognition accuracies. Previous work [1-5] has proven that image variations of faces under varying illuminations could be modeled by linear spaces while view spaces which resulted from varying head poses are considered nonlinear [6]. Manifold learning [6, 7], bilinear models [8-10], linear dimensionality reduction [11, 12], and tensor analysis [13-15] have all been applied to solve face recognition problems. Recently deep learning techniques are also employed to solve face recognition problems and have achieved good performances [16-18].
Usually, faces need to be aligned before recognition. There has been lots of works which have been done on face alignment [19-30]. And it is well known that misalignment will result in recognition accuracy drop [20]. In our work, we explore the possibility of face recognition without alignment. In the proposed algorithm, we concentrate on separating factors that might affect recognition accuracy. To be specific, we use bilinear model to separate a varying factor from recognition target. And we prove in experiments that this separation is effective and required.
Bilinear models [8] are usually used to model systems with two factors and each factor itself is linear given the other factor fixed. Bilinear models for 2D image data have been widely used to solve face recognition problems due to their simplicity in formulation. In this category of solutions, factors introduced in a system are modeled as a symmetric bilinear model or asymmetric bilinear model. In this work, we also introduce asymmetric bilinear...