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Face Verification via Class Sparsity Based Supervised Encoding.
- Source :
- IEEE Transactions on Pattern Analysis & Machine Intelligence; Jun2017, Vol. 39 Issue 6, p1273-1280, 8p
- Publication Year :
- 2017
-
Abstract
- 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<alternatives> <inline-graphic xlink:href="vatsa-ieq1-2569436.gif"/></alternatives>-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. [ABSTRACT FROM AUTHOR]
- Subjects :
- ALGORITHMS
MACHINE translating
INDEXING
EDUCATION
MATHEMATICS
Subjects
Details
- Language :
- English
- ISSN :
- 01628828
- Volume :
- 39
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Pattern Analysis & Machine Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 122985183
- Full Text :
- https://doi.org/10.1109/TPAMI.2016.2569436