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Face recognition based on fusion of multi-resolution Gabor features

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Abstract

In this paper, we propose a Gabor-based face recognition method. This method fuses multi-resolution Gabor features of face images at the matching score level. The first implementation scheme of this method directly takes the sum of the matching scores of multi-resolution Gabor features of face images as the final matching score. The second implementation scheme first codes the phase of the Gabor feature and then uses a weighted matching score level fusion algorithm to fuse the magnitude and phase of the Gabor feature. A number of experimental results show that the proposed method has a good performance and outperforms conventional Gabor-based face recognition methods that equally treat all the Gabor features and directly fuse them at the feature level. The experimental result also illustrates that in face recognition, the low-resolution representation of the phase of the Gabor feature such as the code of the phase is more discriminative than the phase itself. The codes of our method will be available at http://www.yongxu.org/lunwen.html.

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Acknowledgments

This article is partly supported by Key Laboratory of Network Oriented Intelligent Computation, Program for New Century Excellent Talents in University (Grand Nos. NCET-08-0156 and NCET-08-0155), Natural Scientific Research Innovation Foundation in Harbin Institute of Technology (HIT. NSRIF. 2009130) and National Nature Science Committee of China (Grand Nos. 61071179, 90820306, 60902099 and 61001037).

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Authors and Affiliations

  1. Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China

    Yong Xu, Zhengming Li & Jeng-Shyang Pan

  2. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, People’s Republic of China

    Jing-Yu Yang

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  1. Yong Xu
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  2. Zhengming Li
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  3. Jeng-Shyang Pan
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  4. Jing-Yu Yang
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Correspondence to Yong Xu.

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Xu, Y., Li, Z., Pan, JS. et al. Face recognition based on fusion of multi-resolution Gabor features. Neural Comput & Applic 23, 1251–1256 (2013). https://doi.org/10.1007/s00521-012-1066-3

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  • DOI: https://doi.org/10.1007/s00521-012-1066-3

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