A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 3 Issue 2
Apr.  2016

IEEE/CAA Journal of Automatica Sinica

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    CiteScore: 11.2, Top 5% (Q1)
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Article Contents
Xiaoying Wang, Haifeng Hu and Jianquan Gu, "Pose Robust Low-resolution Face Recognition via Coupled Kernel-based Enhanced Discriminant Analysis," IEEE/CAA J. of Autom. Sinica, vol. 3, no. 2, pp. 203-212, 2016.
Citation: Xiaoying Wang, Haifeng Hu and Jianquan Gu, "Pose Robust Low-resolution Face Recognition via Coupled Kernel-based Enhanced Discriminant Analysis," IEEE/CAA J. of Autom. Sinica, vol. 3, no. 2, pp. 203-212, 2016.

Pose Robust Low-resolution Face Recognition via Coupled Kernel-based Enhanced Discriminant Analysis

Funds:

This work was supported by National Natural Science Foundation of China (60802069,61273270), the Fundamental Research Funds for the Central Universities of China, Natural Science Foundation of Guangdong Province (2014A030313173), and Science and Technology Program of Guangzhou (2014Y2-00165,2014J4100114,2014J4100095).

  • Most face recognition techniques have been successful in dealing with high-resolution (HR) frontal face images. However, real-world face recognition systems are often confronted with the low-resolution (LR) face images with pose and illumination variations. This is a very challenging issue, especially under the constraint of using only a single gallery image per person. To address the problem, we propose a novel approach called coupled kernel-based enhanced discriminant analysis (CKEDA). CKEDA aims to simultaneously project the features from LR non-frontal probe images and HR frontal gallery ones into a common space where discrimination property is maximized. There are four advantages of the proposed approach: 1) by using the appropriate kernel function, the data becomes linearly separable, which is beneficial for recognition; 2) inspired by linear discriminant analysis (LDA), we integrate multiple discriminant factors into our objective function to enhance the discrimination property; 3) we use the gallery extended trick to improve the recognition performance for a single gallery image per person problem; 4) our approach can address the problem of matching LR non-frontal probe images with HR frontal gallery images, which is difficult for most existing face recognition techniques. Experimental evaluation on the multi-PIE dataset signifies highly competitive performance of our algorithm.

     

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