In this paper, we propose a random network ensemble for face recognition problem, particularly for images with a large appearance variation and with a limited number of training set. In order to reduce the correlation within the network ensemble using a single type of feature extractor and classifier, localized random facial features have been constructed together with internally randomized networks. The ensemble classifier is finally constructed by combining these multiple networks via a sum rule. The proposed method is shown to have a better accuracy(31.5% and 15.3% improvements on AR and EYALEB databases respectively) and a better efficiency than that of the widely used PCA- SVM. © Springer-Verlag Berlin Heidelberg 2009.
CITATION STYLE
Choi, K., Toh, K. A., & Byun, H. (2009). A random network ensemble for recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5558 LNCS, pp. 92–101). https://doi.org/10.1007/978-3-642-01793-3_10
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