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Issue No. 06 - June (2017 vol. 39)
ISSN: 0162-8828
pp: 1273-1280
Angshul Majumdar , IIIT-Delhi, New Delhi, India
Richa Singh , IIIT-Delhi, New Delhi, India
Mayank Vatsa , IIIT-Delhi, New Delhi, India
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 $_$l_{2,1}$_$ -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.
Face, Encoding, Training, Convolution, Machine learning, Face recognition, Algorithm design and analysis

A. Majumdar, R. Singh and M. Vatsa, "Face Verification via Class Sparsity Based Supervised Encoding," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 39, no. 6, pp. 1273-1280, 2017.
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