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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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 |
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Face recognition; sparse representation; trace lasso |
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Face recognition; sparse representation; trace lasso Lai, Jian Jiang, Xudong Supervised trace lasso for robust face recognition |
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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 |
_version_ |
1681043447227613184 |