Single Sample Face Recognition by Sparse Recovery of Deep-Learned LDA Features. Bodini, M., D’Amelio, A., Grossi, G., Lanzarotti, R., & Lin, J. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018. doi abstract bibtex 4 downloads 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.
@inproceedings{
title = {Single Sample Face Recognition by Sparse Recovery of Deep-Learned LDA Features},
type = {inproceedings},
year = {2018},
id = {fe9170d9-0dc3-3a47-8c52-c7d4e66d1c58},
created = {2020-05-28T14:08:00.726Z},
file_attached = {false},
profile_id = {6bce6ab9-03b5-36ad-a474-26e482dc52c3},
last_modified = {2020-05-28T14:08:00.726Z},
read = {false},
starred = {false},
authored = {true},
confirmed = {true},
hidden = {false},
private_publication = {false},
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 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.},
bibtype = {inproceedings},
author = {Bodini, Matteo and D’Amelio, Alessandro and Grossi, Giuliano and Lanzarotti, Raffaella and Lin, Jianyi},
doi = {10.1007/978-3-030-01449-0_25},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}
Downloads: 4
{"_id":"infxyzKuawyusJXLd","bibbaseid":"bodini-damelio-grossi-lanzarotti-lin-singlesamplefacerecognitionbysparserecoveryofdeeplearnedldafeatures-2018","authorIDs":["EF82ixfaSgYFdM2FT"],"author_short":["Bodini, M.","D’Amelio, A.","Grossi, G.","Lanzarotti, R.","Lin, J."],"bibdata":{"title":"Single Sample Face Recognition by Sparse Recovery of Deep-Learned LDA Features","type":"inproceedings","year":"2018","id":"fe9170d9-0dc3-3a47-8c52-c7d4e66d1c58","created":"2020-05-28T14:08:00.726Z","file_attached":false,"profile_id":"6bce6ab9-03b5-36ad-a474-26e482dc52c3","last_modified":"2020-05-28T14:08:00.726Z","read":false,"starred":false,"authored":"true","confirmed":"true","hidden":false,"private_publication":false,"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 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.","bibtype":"inproceedings","author":"Bodini, Matteo and D’Amelio, Alessandro and Grossi, Giuliano and Lanzarotti, Raffaella and Lin, Jianyi","doi":"10.1007/978-3-030-01449-0_25","booktitle":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","bibtex":"@inproceedings{\n title = {Single Sample Face Recognition by Sparse Recovery of Deep-Learned LDA Features},\n type = {inproceedings},\n year = {2018},\n id = {fe9170d9-0dc3-3a47-8c52-c7d4e66d1c58},\n created = {2020-05-28T14:08:00.726Z},\n file_attached = {false},\n profile_id = {6bce6ab9-03b5-36ad-a474-26e482dc52c3},\n last_modified = {2020-05-28T14:08:00.726Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n 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 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.},\n bibtype = {inproceedings},\n author = {Bodini, Matteo and D’Amelio, Alessandro and Grossi, Giuliano and Lanzarotti, Raffaella and Lin, Jianyi},\n doi = {10.1007/978-3-030-01449-0_25},\n booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}","author_short":["Bodini, M.","D’Amelio, A.","Grossi, G.","Lanzarotti, R.","Lin, J."],"biburl":"https://bibbase.org/service/mendeley/6bce6ab9-03b5-36ad-a474-26e482dc52c3","bibbaseid":"bodini-damelio-grossi-lanzarotti-lin-singlesamplefacerecognitionbysparserecoveryofdeeplearnedldafeatures-2018","role":"author","urls":{},"metadata":{"authorlinks":{}},"downloads":4},"bibtype":"inproceedings","biburl":"https://bibbase.org/service/mendeley/6bce6ab9-03b5-36ad-a474-26e482dc52c3","creationDate":"2019-07-15T22:21:26.312Z","downloads":4,"keywords":[],"search_terms":["single","sample","face","recognition","sparse","recovery","deep","learned","lda","features","bodini","d’amelio","grossi","lanzarotti","lin"],"title":"Single Sample Face Recognition by Sparse Recovery of Deep-Learned LDA Features","year":2018,"dataSources":["uTaF5sv54H7Hk6LZw","KrZZ3KEZ3zvS84wws","oxeFGHQt5jKA38N7c","2252seNhipfTmjEBQ"]}