Abstract
In this paper, we propose a cascaded face-identification framework for enhanced recognition performance. During each stage, the classification is dynamically optimized to discriminate a set of promising candidates selected from the previous stage, thereby incrementally increasing the overall discriminating performance. To ensure improved performance, the base classifier at each stage should satisfy two key properties: (1) adaptivity to specific populations, and (2) high training and identification efficiency such that dynamic training can be performed for each test case. To this end, we adopt a base classifier with (1) dynamic person-specific feature selection, and (2) voting of an ensemble of simple classifiers based on selected features. Our experiments show that the cascaded framework effectively improves the face recognition rate by up to 5% compared to a single stage algorithm, and it is 2-3% better than established well-known face recognition algorithms.
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© 2005 Springer-Verlag Berlin Heidelberg
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Zuo, F., de With, P.H.N., van der Veen, M. (2005). Multistage Face Recognition Using Adaptive Feature Selection and Classification. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_3
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DOI: https://doi.org/10.1007/11558484_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29032-2
Online ISBN: 978-3-540-32046-3
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