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Face Verification via Class Sparsity Based Supervised Encoding
Published in IEEE Computer Society
PMID: 27214891
Volume: 39
Issue: 6
Pages: 1273 - 1280
Autoencoders are deep learning architectures that learn feature representation by minimizing the reconstruction error. Using an autoencoder as baseline, this paper presents a novel formulation for a class sparsity based supervised encoder, termed as CSSE. We postulate that features from the same class will have a common sparsity pattern/support in the latent space. Therefore, in the formulation of the autoencoder, a supervision penalty is introduced as a joint-sparsity promoting l2,1-norm. The formulation of CSSE is derived for a single hidden layer and it is applied for multiple hidden layers using a greedy layer-by-layer learning approach. The proposed CSSE approach is applied for learning face representation and verification experiments are performed on the LFW and PaSC face databases. The experiments show that the proposed approach yields improved results compared to autoencoders and comparable results with state-of-the-art face recognition algorithms. © 2016 IEEE.
About the journal
JournalData powered by TypesetIEEE Transactions on Pattern Analysis and Machine Intelligence
PublisherData powered by TypesetIEEE Computer Society