• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Generic Training Set based Multimanifold Discriminant Learning for Single Sample Face Recognition


Abstract

Face recognition (FR) with a single sample per person (SSPP) is common in real-world face recognition applications. In this scenario, it is hard to predict intra-class variations of query samples by gallery samples due to the lack of sufficient training samples. Inspired by the fact that similar faces have similar intra-class variations, we propose a virtual sample generating algorithm called k nearest neighbors based virtual sample generating (kNNVSG) to enrich intra-class variation information for training samples. Furthermore, in order to use the intra-class variation information of the virtual samples generated by kNNVSG algorithm, we propose image set based multimanifold discriminant learning (ISMMDL) algorithm. For ISMMDL algorithm, it learns a projection matrix for each manifold modeled by the local patches of the images of each class, which aims to minimize the margins of intra-manifold and maximize the margins of inter-manifold simultaneously in low-dimensional feature space. Finally, by comprehensively using kNNVSG and ISMMDL algorithms, we propose k nearest neighbor virtual image set based multimanifold discriminant learning (kNNMMDL) approach for single sample face recognition (SSFR) tasks. Experimental results on AR, Multi-PIE and LFW face datasets demonstrate that our approach has promising abilities for SSFR with expression, illumination and disguise variations.


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Cite this article

[IEEE Style]
Xiwei Dong, Fei Wu and Xiao-Yuan Jing, "Generic Training Set based Multimanifold Discriminant Learning for Single Sample Face Recognition," KSII Transactions on Internet and Information Systems, vol. 12, no. 1, pp. 368-391, 2018. DOI: 10.3837/tiis.2018.01.018

[ACM Style]
Dong, X., Wu, F., and Jing, X. 2018. Generic Training Set based Multimanifold Discriminant Learning for Single Sample Face Recognition. KSII Transactions on Internet and Information Systems, 12, 1, (2018), 368-391. DOI: 10.3837/tiis.2018.01.018