Abstract
Single Sample Per Person (SSPP) Face Recognition is receiving a significant attention due to the challenges it opens especially when conceived for real applications under unconstrained environments. In this paper we propose a solution combining the effectiveness of deep convolutional neural networks (DCNN) feature characterization, the discriminative capability of linear discriminant analysis (LDA), and the efficacy of a sparsity based classifier built on the \(k\)-LiMapS algorithm. Experiments on the public LFW dataset prove the method robustness to solve the SSPP problem, outperforming several state-of-the-art methods.
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- 1.
A demo code is available on the website: https://github.com/phuselab/SSPP-face_recognition.
- 2.
Strictly speaking the \(\ell _0\)-norm is not actually a norm, it is a cardinality function counting the number of nonzero elements in a vector.
- 3.
For a given of vector \(\alpha \), the support \({{\mathrm{ supp}}}(\alpha )\) is the index pool of nonzero entries of \(\alpha \).
- 4.
Here we have simplified the notation to refer to the sparse solutions \(\alpha _{l,f}\) to \(\alpha _j\), knowing that the couple set (l, f) has cardinality d.
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Bodini, M., D’Amelio, A., Grossi, G., Lanzarotti, R., Lin, J. (2018). Single Sample Face Recognition by Sparse Recovery of Deep-Learned LDA Features. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2018. Lecture Notes in Computer Science(), vol 11182. Springer, Cham. https://doi.org/10.1007/978-3-030-01449-0_25
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