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http://dx.doi.org/10.3837/tiis.2016.12.018

Few Samples Face Recognition Based on Generative Score Space  

Wang, Bin (College of Information, Mechanical and Electrical Engineering, Shanghai Normal University)
Wang, Cungang (School of Computer Science, Liaocheng University)
Zhang, Qian (College of Information, Mechanical and Electrical Engineering, Shanghai Normal University)
Huang, Jifeng (College of Information, Mechanical and Electrical Engineering, Shanghai Normal University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.10, no.12, 2016 , pp. 5464-5484 More about this Journal
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.
Keywords
Generative score space; face representation; few samples face recognition;
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