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Transformed Principal Gradient Orientation for Robust and Precise Batch Face Alignment

  • Weihong Deng
  • Jiani Hu
  • Liu Liu
  • Jun Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9006)

Abstract

This paper addresses the problem of simultaneously aligning a batch of linearly correlated images despite large misalignment, severe illumination and occlusion. Our algorithm assumes that the gradient orientation of images, if correctly aligned, can be robustly represented by an underlying transformed principal gradient orientation (TPGO) subspace. With such a linear representation prior, the proposed method connects PGO subspace learning, gradient orientation reconstruction, and batch alignment in a unified framework with an efficient alternating optimization solution. Besides inherent robustness from the gradient orientation and the low-rank structure, TPGO maintains the pixel-accurate registration precision and the efficient optimization of Lucas & Kanade framework. Experimental results show TPGO based batch alignment is more precise and robust than the state-of-the-art methods such as RASL and SIFT feature base Congealing. Moreover, integrated with a SIFT based pre-alignment procedure, TPGO is able to align a large number of images of multiple objects with large deviation, illumination, and occlusion in the precision that surpasses the handcrafted alignments (provided by the standard database distributions), in term of our face recognition experiments on the Extended Yale B, AR and FERET databases.

Keywords

Gradient Orientation Sift Feature Subspace Learning FERET Database Automatic Face Recognition 
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.

Supplementary material

336669_1_En_46_MOESM1_ESM.pdf (1 mb)
Supplementary material (pdf 1,041 KB)

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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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