Abstract
A self-adaptive radial basis function neural network (RBFNN)-based recognition of human faces has been proposed in this paper. Conventionally, all the hidden layer neurons of an RBFNN are considered to generate outputs at the output layer. In this work, a confidence measure has been imposed to select a subset of the hidden layer neurons to generate outputs at the output layer, thereby making the RBFNN as self-adaptive for choosing hidden layer neurons to be considered while generating outputs at the output layer. This process also reduces the computation time at the output layer of the RBFNN by neglecting the ineffective RBFs. The performance of the proposed method has been evaluated on the ORL and the UMIST face databases. The experimental results indicate that the proposed method can achieve excellent recognition rates and outperform some of the traditional face recognition approaches.
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Gharai, S. et al. (2006). Self-adaptive RBF Neural Networks for Face Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919476_36
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DOI: https://doi.org/10.1007/11919476_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48628-2
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