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

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-01, Vol.19 (1), p.146
Main Authors: Cuculo, Vittorio, D'Amelio, Alessandro, Grossi, Giuliano, Lanzarotti, Raffaella, Lin, Jianyi
Format: Article
Language:English
Subjects:
Algorithms
Datasets
Deep Convolutional Neural Network (DCNN) features
dictionary learning
Face recognition
Facial recognition technology
Feature recognition
Hardness
Illumination
Image acquisition
International conferences
Neural networks
optimal directions (MOD)
single sample per person
sparse recovery
Sparsity
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Summary: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 ℓ 0 -norm minimization algorithm called -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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19010146