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Low-Resolution Face Recognition via Coupled Locality Preserving Mappings

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4 Author(s)
Bo Li ; Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin, China ; Hong Chang ; Shiguang Shan ; Xilin Chen

Practical face recognition systems are sometimes confronted with low-resolution face images. Traditional two-step methods solve this problem through employing super-resolution (SR). However, these methods usually have limited performance because the target of SR is not absolutely consistent with that of face recognition. Moreover, time-consuming sophisticated SR algorithms are not suitable for real-time applications. To avoid these limitations, we propose a novel approach for LR face recognition without any SR preprocessing. Our method based on coupled mappings (CMs), projects the face images with different resolutions into a unified feature space which favors the task of classification. These CMs are learned through optimizing the objective function to minimize the difference between the correspondences (i.e., low-resolution image and its high-resolution counterpart). Inspired by locality preserving methods for dimensionality reduction, we introduce a penalty weighting matrix into our objective function. Our method significantly improves the recognition performance. Finally, we conduct experiments on publicly available databases to verify the efficacy of our algorithm.

Published in:

Signal Processing Letters, IEEE  (Volume:17 ,  Issue: 1 )
Biometrics Compendium, IEEE