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.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11182 LNCS, pp. 297–308). Springer Verlag. https://doi.org/10.1007/978-3-030-01449-0_25
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