Single Sample Face Recognition by Sparse Recovery of Deep-Learned LDA Features
| Autor: | Matteo Bodini, Giuliano Grossi, Raffaella Lanzarotti, Alessandro D’Amelio, Jianyi Lin |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Pattern recognition Single sample 0102 computer and information sciences 02 engineering and technology Linear discriminant analysis 01 natural sciences Facial recognition system Convolutional neural network ComputingMethodologies_PATTERNRECOGNITION Discriminative model 010201 computation theory & mathematics Robustness (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) |
| Zdroj: | Advanced Concepts for Intelligent Vision Systems ISBN: 9783030014483 ACIVS |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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