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: | , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/81358 http://hdl.handle.net/10220/39536 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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