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Face recognition via fast dense correspondence

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Abstract

Face recognition plays a significant role in computer vision. It is well know that facial images are complex stimuli signals that suffer from non-rigid deformations, including misalignment, orientation, pose changes, and variations of facial expression, etc. In order to address these variations, this paper introduces an improved sparse-representation based face recognition method, which constructs dense pixel correspondences between training and testing facial samples. Specifically, we first construct a deformable spatial pyramid graph model that simultaneously regularizes matching consistency at multiple spatial extents - ranging from an entire image, though coarse grid cells, to every single pixel. Secondly, a matching energy function is designed to perform face alignment based on dense pixel correspondence, which is very effective to address the issue of non-rigid deformations. Finally, a novel coarse-to-fine matching scheme is designed so that we are able to speed up the optimization of the matching energy function. After the training samples are aligned with respect to testing samples, an improved sparse representation model is employed to perform face recognition. The experimental results demonstrate the superiority of the proposed method over other methods on ORL, AR, and LFWCrop datasets. Especially, the proposed approach improves nearly 4.4 % in terms of recognition accuracy and runs nearly 10 times faster than previous sparse approximation methods.

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Acknowledgments

The authors would like to thank the associated editor and all the anonymous reviewers for their valuable comments and suggestions. This work was partly supported by the National Science Foundation (Grant No. IIS-1302164), and the National Natural Science Foundation of China (Grant No. 61401228, 61402122, 61571240, 61501247, 61501259, 61671253), and China Postdoctoral Science Foundation (Grant No. 2015M581841), and Natural Science Foundation of Jiangsu Province (Grant No. BK20160908), and Postdoctoral Science Foundation of Jiangsu Province (Grant No. 1501019A), and the Priority Academic Program Development of Jiangsu Higer Education Institutions(PAPD), and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology(CICAEET), and Nanjing University of Information Science and Technology Research Foundation for Talented Scholars (Grant No. 2015r014).

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Authors and Affiliations

  1. Key Lab of Ministry of Education for Broad Band Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Nanjing, China

    Quan Zhou, Yawen Fan, Hu Zhu, Xiaofu Wu & Weiping Zhu

  2. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China

    Quan Zhou & Wenbin Yu

  3. The State Grid HuBei Information and Telecommunication Company, Wuhan, China

    Cheng Zhang

  4. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, China

    Wenbin Yu

  5. School of Big Data and Computer Science, Guizhou Normal University, Guiyang, China

    Weihua Ou

  6. Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada

    Weiping Zhu

  7. Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA

    Longin Jan Latecki

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  1. Quan Zhou
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  2. Cheng Zhang
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  7. Weihua Ou
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  9. Longin Jan Latecki
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Correspondence to Quan Zhou.

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Zhou, Q., Zhang, C., Yu, W. et al. Face recognition via fast dense correspondence. Multimed Tools Appl 77, 10501–10519 (2018). https://doi.org/10.1007/s11042-017-4569-1

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  • DOI: https://doi.org/10.1007/s11042-017-4569-1

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