Sparse Representation-based Face RecognitionMethod by LDA Feature Extraction
[1]ZHANG Yong,DANG Lan-xue.Sparse Representation-based Face RecognitionMethod by LDA Feature Extraction[J].Journal of Zhengzhou University (Engineering Science),2015,36(02):94-98.[doi:10.3969/j.issn.1671-6833.2015.02.021]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
36
Number of periods:
2015 02
Page number:
94-98
Column:
Public date:
2015-04-30
- Title:
- Sparse Representation-based Face RecognitionMethod by LDA Feature Extraction
- Keywords:
- LDA; sparse representation ; feature extraction ; face recognition
- CLC:
- TP391
- Abstract:
- To solve the problem that the features extracted by randomfaces method have weak discriminative a-bility in sparse representation-based classification ( SRC),a sparse representation - based face recognitionmethod by linear discriminant analysis ( LDA) feature extraction was proposed. Firstly,LDA is used to solvethe optimal discriminative projective subspace,and then the training samples are projected onto the subspaceto extract the features of the training samples. Using the features of the trainings samples as the dictionary, thefeatures of the test sample can be sparsely represent as linear combination of the atoms of the dictionary. Fur-thermore,using the sparse coefficients associated with the special class,this method approximates the featuresof the test sample and calculates the reconstruction error between the features of the test sample with its ap-proximation associated with the special class. Based on the reconstruction error associated with special class,the test sample can be classified accurately. Experimental results on Extend Yale B and CMU PIE face data-bases show that face recognition method proposed in this paper has a good performance.
- References:
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