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 kk-LiMapS algorithm. Experiments on the public LFW dataset prove the method robustness to solve the SSPP problem, outperforming several state-of-the-art methods.

Single Sample Face Recognition by Sparse Recovery of Deep-Learned LDA Features / M. Bodini, A. D'Amelio, G. Grossi, R. Lanzarotti, J. Lin (LECTURE NOTES IN COMPUTER SCIENCE). - In: Advanced Concepts for Intelligent Vision Systems / [a cura di] J. Blanc-Talon, D. Helbert, W. Philips, D. Popescu, P. Scheunders. - [s.l] : Springer, 2018. - ISBN 9783030014483. - pp. 297-308 (( Intervento presentato al 19. convegno ACIVS tenutosi a Poitiers nel 2018 [10.1007/978-3-030-01449-0_25].

Single Sample Face Recognition by Sparse Recovery of Deep-Learned LDA Features

M. Bodini;A. D'Amelio;G. Grossi;R. Lanzarotti;J. Lin
2018

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 kk-LiMapS algorithm. Experiments on the public LFW dataset prove the method robustness to solve the SSPP problem, outperforming several state-of-the-art methods.
Settore INF/01 - Informatica
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
2018
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/613252
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