Abstract
In this paper, a new algorithm named Adaptive Weighted Label Propagation (AWLP) which explores the complementary property among sub-patterns from the same face image is proposed for local matching based face recognition. The proposed AWLP first partitions the face images into several smaller sub-images. Then, multiple similarity graphs are constructed for different sub-pattern sets. At last, in order to take correlation among different sub-patterns into account, the graphs obtained by various sub-pattern sets are combined and the procedures of label prediction and graph weight learning are integrated into a unified framework to propagate the class information of the labeled samples to unlabeled ones. Moreover, a simple yet efficient iterative update algorithm is also proposed to solve our AWLP. Extensive experiments on three face benchmark databases show that AWLP has very competitive performance with the state-of-the-art algorithms.
Keywords
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Guo, Y., Li, X., Yi, Y., Wei, Y., Wang, J. (2014). Adaptive Weighted Label Propagation for Local Matching Based Face Recognition. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds) Biometric Recognition. CCBR 2014. Lecture Notes in Computer Science, vol 8833. Springer, Cham. https://doi.org/10.1007/978-3-319-12484-1_8
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DOI: https://doi.org/10.1007/978-3-319-12484-1_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12483-4
Online ISBN: 978-3-319-12484-1
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