Supervised trace lasso for robust face recognition

In this paper, we address the robust face recognition problem. Recently, trace lasso was introduced as an adaptive norm based on the training data. It uses the correlation among the training samples to tackle the instability problem of sparse representation coding. Trace lasso naturally clusters...

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Main Authors: Lai, Jian, Jiang, Xudong
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2016
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Online Access:https://hdl.handle.net/10356/81358
http://hdl.handle.net/10220/39536
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-813582020-03-07T13:24:44Z Supervised trace lasso for robust face recognition Lai, Jian Jiang, Xudong School of Electrical and Electronic Engineering 2014 IEEE International Conference on Multimedia and Expo (ICME) Face recognition; sparse representation; trace lasso In this paper, we address the robust face recognition problem. Recently, trace lasso was introduced as an adaptive norm based on the training data. It uses the correlation among the training samples to tackle the instability problem of sparse representation coding. Trace lasso naturally clusters the highly correlated data together. However, the face images with similar variations, such as illumination or expression, often have higher correlation than those from the same class. In this case, the result of trace lasso is contradictory to the goal of recognition, which is to cluster the samples according to their identities. Therefore, trace lasso is not a good choice for face recognition task. In this work, we propose a supervised trace lasso (STL) framework by employing the class label information. To represent the query sample, the proposed STL approach seeks the sparsity of the number of classes instead of the number of training samples. This directly coincides with the objective of the classification. Furthermore, an efficient algorithm to solve the optimization problem of proposed method is given. The extensive experimental results have demonstrated the effectiveness of the proposed framework. Accepted version 2016-01-04T05:52:57Z 2019-12-06T14:29:11Z 2016-01-04T05:52:57Z 2019-12-06T14:29:11Z 2014 Conference Paper Lai, J., & Jiang, X. (2014). Supervised trace lasso for robust face recognition. 2014 IEEE International Conference on Multimedia and Expo (ICME), 1-6. https://hdl.handle.net/10356/81358 http://hdl.handle.net/10220/39536 10.1109/ICME.2014.6890246 en © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/ICME.2014.6890246]. 6 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Face recognition; sparse representation; trace lasso
spellingShingle Face recognition; sparse representation; trace lasso
Lai, Jian
Jiang, Xudong
Supervised trace lasso for robust face recognition
description In this paper, we address the robust face recognition problem. Recently, trace lasso was introduced as an adaptive norm based on the training data. It uses the correlation among the training samples to tackle the instability problem of sparse representation coding. Trace lasso naturally clusters the highly correlated data together. However, the face images with similar variations, such as illumination or expression, often have higher correlation than those from the same class. In this case, the result of trace lasso is contradictory to the goal of recognition, which is to cluster the samples according to their identities. Therefore, trace lasso is not a good choice for face recognition task. In this work, we propose a supervised trace lasso (STL) framework by employing the class label information. To represent the query sample, the proposed STL approach seeks the sparsity of the number of classes instead of the number of training samples. This directly coincides with the objective of the classification. Furthermore, an efficient algorithm to solve the optimization problem of proposed method is given. The extensive experimental results have demonstrated the effectiveness of the proposed framework.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Lai, Jian
Jiang, Xudong
format Conference or Workshop Item
author Lai, Jian
Jiang, Xudong
author_sort Lai, Jian
title Supervised trace lasso for robust face recognition
title_short Supervised trace lasso for robust face recognition
title_full Supervised trace lasso for robust face recognition
title_fullStr Supervised trace lasso for robust face recognition
title_full_unstemmed Supervised trace lasso for robust face recognition
title_sort supervised trace lasso for robust face recognition
publishDate 2016
url https://hdl.handle.net/10356/81358
http://hdl.handle.net/10220/39536
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