Abstract
In this paper, we propose a novel approach called coupled kernel fisher discriminative analysis (CKFDA) based on simultaneous discriminant analysis (SDA) for LR face recognition. Firstly, the high-resolution (HR) and low-resolution (LR) training samples are respectively mapped into two different high-dimensional feature spaces by using kernel functions. Then CKFDA learns two mappings from the kernel images to a common subspace where discrimination property is maximized. Finally, similarity measure is used for classification. Experiments are conducted on publicly available databases to demonstrate the efficacy of our algorithm.
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Wang, X., Liu, L., Hu, H. (2013). Coupled Kernel Fisher Discriminative Analysis for Low-Resolution Face Recognition. In: Sun, Z., Shan, S., Yang, G., Zhou, J., Wang, Y., Yin, Y. (eds) Biometric Recognition. CCBR 2013. Lecture Notes in Computer Science, vol 8232. Springer, Cham. https://doi.org/10.1007/978-3-319-02961-0_10
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DOI: https://doi.org/10.1007/978-3-319-02961-0_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02960-3
Online ISBN: 978-3-319-02961-0
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