Face recognition – combine generic and specific solutions

Authors:
Jie Wang;Juwei Lu;K. N. Plataniotis;A. N. Venetsanopoulos
Affiliations:
Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada;Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada;Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada;Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
Venue:
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Year:
2005

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Abstract

In many realistic face recognition applications, such as surveillance photo identification, the subjects of interest usually have only a limited number of image samples a-priori. This makes the recognition a difficult task, especially when only one image sample is available for each subject. In such a case, the performance of many well known face recognition algorithms will deteriorate rapidly and some of the algorithms even fail to apply. In this paper, we introduced a novel scheme to solve the one training sample problem by combining a specific solution learned from the samples of interested subjects and a generic solution learned from the samples of many other subjects. A multi-learner framework is firstly applied to generate and combine a set of generic base learners followed by a second combination with the specific learner. Extensive experiments based on the FERET database suggests that in the scenario considered here, the proposed solution significantly boosts the recognition performance.