Abstract
Sparse representation technique has been successfully employed to solve face recognition task. Though current sparse representation based classifier proves to achieve high classification accuracy, it implicitly assumes that the losses of all misclassifications are the same. However, in many real-world applications, different misclassifications could lead to different losses. Driven by this concern, we propose in this paper a sparse cost-sensitive classifier for face recognition. Our approach uses probabilistic model of sparse representation to estimate the posterior probabilities of a testing sample, calculates all the misclassification losses via the posterior probabilities and then predicts the class label by minimizing the losses. Experimental results on the public AR and FRGC face databases validate the efficacy of the proposed approach.
Original language | English |
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Title of host publication | ICIP 2011 |
Subtitle of host publication | 2011 18th IEEE International Conference on Image Processing |
Pages | 1773-1776 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 1 Dec 2011 |
Event | 2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium Duration: 11 Sept 2011 → 14 Sept 2011 |
Conference
Conference | 2011 18th IEEE International Conference on Image Processing, ICIP 2011 |
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Country/Territory | Belgium |
City | Brussels |
Period | 11/09/11 → 14/09/11 |
Keywords
- Cost-sensitive learning
- face recognition
- sparse cost-sensitive classifier
- sparse representation
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Signal Processing