Under-sampled face recognition via intra-class variant dictionary modelling

Xiao Luan; Yan Kong; Mi Tang; Linghui Liu

doi:10.1117/12.2589446

27 January 2021 Under-sampled face recognition via intra-class variant dictionary modelling

Xiao Luan, Yan Kong, Mi Tang, Linghui Liu

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117201I (2021) https://doi.org/10.1117/12.2589446
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Many face algorithms require a relatively large number of training samples. In practice, they often face the challenge of inadequate training samples, which reduces their recognition accuracy. Motivated by the extended sparse representation-based classification (ESRC), we propose an improved method to address the problem of under-sampled face recognition. We show that intra-class variant dictionary plays a significant role in feature extraction. Firstly, we propose to use the robust principal component analysis (RPCA) to model the sparse part of face images as intra-class variant dictionary, so that the various changes between faces can be well captured. Secondly, we incorporate the intraclass variant dictionary into the framework of ESRC. Experimental results on the AR and Extended Yale B databases show that our method outperforms other competitors either in the case of cross database recognition or one sample per class.

Citation Download Citation

Xiao Luan, Yan Kong, Mi Tang, and Linghui Liu "Under-sampled face recognition via intra-class variant dictionary modelling", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117201I (27 January 2021); https://doi.org/10.1117/12.2589446

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