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Open AccessArticle

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks

Graduate School of Informatices, Kyoto University, Kyoto 606-8501, Japan
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Sensors 2014, 14(12), 23509-23538; https://doi.org/10.3390/s141223509
Received: 27 September 2014 / Revised: 14 November 2014 / Accepted: 26 November 2014 / Published: 8 December 2014
(This article belongs to the Special Issue Sensor Computing for Mobile Security and Big Data Analytics)
Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the “small sample size” (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0–1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system. View Full-Text
Keywords: multi-camera network; person re-identification; small sample size; generic learning-based ensemble; optimal solution; diversity/accuracy dilemma multi-camera network; person re-identification; small sample size; generic learning-based ensemble; optimal solution; diversity/accuracy dilemma
MDPI and ACS Style

Zhang, C.; Liang, X.; Matsuyama, T. Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks. Sensors 2014, 14, 23509-23538.

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