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

Few Samples Face Recognition Based on Generative Score Space

Vol. 10, No.12, December 31, 2016
10.3837/tiis.2016.12.018, Download Paper (Free):

Abstract

Few samples face recognition has become a highly challenging task due to the limitation of available labeled samples. As two popular paradigms in face image representation, sparse component analysis is highly robust while parts-based paradigm is particularly flexible. In this paper, we propose a probabilistic generative model to incorporate the strengths of the two paradigms for face representation. This model finds a common spatial partition for given images and simultaneously learns a sparse component analysis model for each part of the partition. The two procedures are built into a probabilistic generative model. Then we derive the score function (i.e. feature mapping) from the generative score space. A similarity measure is defined over the derived score function for few samples face recognition. This model is driven by data and specifically good at representing face images. The derived generative score function and similarity measure encode information hidden in the data distribution. To validate the effectiveness of the proposed method, we perform few samples face recognition on two face datasets. The results show its advantages.


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Cite this article

[IEEE Style]
Bin Wang, Cungang Wang, Qian Zhang and Jifeng Huang, "Few Samples Face Recognition Based on Generative Score Space," KSII Transactions on Internet and Information Systems, vol. 10, no. 12, pp. 6027-6047, 2016. DOI: 10.3837/tiis.2016.12.018

[ACM Style]
Wang, B., Wang, C., Zhang, Q., and Huang, J. 2016. Few Samples Face Recognition Based on Generative Score Space. KSII Transactions on Internet and Information Systems, 10, 12, (2016), 6027-6047. DOI: 10.3837/tiis.2016.12.018