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Regularized deep learning for face recognition with weight variations
S. Nagpal, M. Singh, ,
Published in Institute of Electrical and Electronics Engineers Inc.
Volume: 3
Pages: 3010 - 3018
Body weight variations are an integral part of a person's aging process. However, the lack of association between the age and the weight of an individual makes it challenging to model these variations for automatic face recognition. In this paper, we propose a regularizer-based approach to learn weight invariant facial representations using two different deep learning architectures, namely, sparse-stacked denoising autoencoders and deep Boltzmann machines.We incorporate a body-weight aware regularization parameter in the loss function of these architectures to help learn weight-aware features. The experiments performed on the extended WIT database show that the introduction of weight aware regularization improves the identification accuracy of the architectures both with and without dropout. © 2015 IEEE.
About the journal
JournalData powered by TypesetIEEE Access
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open AccessNo