Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81358
Title: Supervised trace lasso for robust face recognition
Authors: Lai, Jian
Jiang, Xudong
Keywords: Face recognition; sparse representation; trace lasso
Issue Date: 2014
Source: Lai, J., & Jiang, X. (2014). Supervised trace lasso for robust face recognition. 2014 IEEE International Conference on Multimedia and Expo (ICME), 1-6.
Conference: 2014 IEEE International Conference on Multimedia and Expo (ICME)
Abstract: 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.
URI: https://hdl.handle.net/10356/81358
http://hdl.handle.net/10220/39536
DOI: 10.1109/ICME.2014.6890246
Schools: School of Electrical and Electronic Engineering 
Rights: © 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].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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