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Learning with Multiple Orientations and Scales for Face Recognition

  • Chuan-Xian Ren
  • Dao-Qing Dai
  • Zhao-Rong Lai
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)

Abstract

A novel feature fusion algorithm using multiple orientations and scales for illumination-robust face recognition is proposed in this paper. For a given image, it will firstly be transformed by a group of Gabor filters, and the transformed coefficients be weighted by a vector, which can be determined by a given discriminant criteria and constrained quadratic programming method, then the weighted sum of these vectors is defined as the feature representation of the facial image. The new method provides a framework to regularize and calculate the complex similarity between different scales and orientations features, and it effectively avoids dimensionality curse problem emerged in some concatenation based feature fusion methods. Meanwhile, our method presents a reasonable approach for sensing discriminant orientations and scales according to the optimized weights. The framework can be extended into general multi-modal pattern recognition problems. Experiments using benchmark databases show that our new method obtains competitive performance and improves the recognition result.

Keywords

Face Recognition Illumination Orientation Scale Regularization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chuan-Xian Ren
    • 1
  • Dao-Qing Dai
    • 1
  • Zhao-Rong Lai
    • 1
  1. 1.Mathematics DepartmentSun Yat-Sen UniversityGuangzhouChina

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