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Open AccessArticle

Robust Single-Sample Face Recognition by Sparsity-Driven Sub-Dictionary Learning Using Deep Features

1
Dipartimento di Informatica, Università degli Studi di Milano, via Celoria 18, 20133 Milano, Italy
2
Department of Mathematics, Khalifa University of Science and Technology, Al Saada Street, PO Box 127788, Abu Dhabi, UAE
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
This article is an extended version of a conference paper previously published “Bodini, M., D’Amelio, A., Grossi, G., Lanzarotti, R., Lin, J. (2018, September). Single Sample Face Recognition by Sparse Recovery of Deep-Learned LDA Features. In International Conference on Advanced Concepts for Intelligent Vision Systems (pp. 297–308). Springer, Cham”.
Sensors 2019, 19(1), 146; https://doi.org/10.3390/s19010146
Received: 25 November 2018 / Revised: 21 December 2018 / Accepted: 27 December 2018 / Published: 3 January 2019
(This article belongs to the Section Physical Sensors)
Face recognition using a single reference image per subject is challenging, above all when referring to a large gallery of subjects. Furthermore, the problem hardness seriously increases when the images are acquired in unconstrained conditions. In this paper we address the challenging Single Sample Per Person (SSPP) problem considering large datasets of images acquired in the wild, thus possibly featuring illumination, pose, face expression, partial occlusions, and low-resolution hurdles. The proposed technique alternates a sparse dictionary learning technique based on the method of optimal direction and the iterative 0 -norm minimization algorithm called k-LiMapS. It works on robust deep-learned features, provided that the image variability is extended by standard augmentation techniques. Experiments show the effectiveness of our method against the hardness introduced above: first, we report extensive experiments on the unconstrained LFW dataset when referring to large galleries up to 1680 subjects; second, we present experiments on very low-resolution test images up to 8 × 8 pixels; third, tests on the AR dataset are analyzed against specific disguises such as partial occlusions, facial expressions, and illumination problems. In all the three scenarios our method outperforms the state-of-the-art approaches adopting similar configurations. View Full-Text
Keywords: face recognition; single sample per person; dictionary learning; optimal directions (MOD); Deep Convolutional Neural Network (DCNN) features; sparse recovery face recognition; single sample per person; dictionary learning; optimal directions (MOD); Deep Convolutional Neural Network (DCNN) features; sparse recovery
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Cuculo, V.; D’Amelio, A.; Grossi, G.; Lanzarotti, R.; Lin, J. Robust Single-Sample Face Recognition by Sparsity-Driven Sub-Dictionary Learning Using Deep Features. Sensors 2019, 19, 146.

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