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Blur-Robust Face Recognition via Transformation Learning

  • Jun Li
  • Chi Zhang
  • Jiani Hu
  • Weihong Deng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9010)

Abstract

This paper introduces a new method for recognizing faces degraded by blur using transformation learning on the image feature. The basic idea is to transform both the sharp images and blurred images to a same feature subspace by the method of multidimensional scaling. Different from the method of finding blur-invariant descriptors, our method learns the transformation which both preserves the manifold structure of the original shape images and, at the same time, enhances the class separability, resulting in a wide applications to various descriptors. Furthermore, we combine our method with subspace-based point spread function (PSF) estimation method to handle cases of unknown blur degree, by applying the feature transformation corresponding to the best matched PSF, where the transformation for each PSF is learned in the training stage. Experimental results on the FERET database show the proposed method achieve comparable performance against the state-of-the-art blur-invariant face recognition methods, such as LPQ and FADEIN.

Keywords

Face Recognition Point Spread Function Training Image Sharp Image Transformation Learn 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was partially sponsored by National Natural Science Foundation of China (NSFC) under Grant No. 61375031, No. 61471048, and No. 61273217. This work was also supported by the Fundamental Research Funds for the Central Universities, Beijing Higher Education Young Elite Teacher Project, and the Program for New Century Excellent Talents in University.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina

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