원문정보
초록
영어
In this paper, we have proposed an optimized sparse representation algorithm based on Log-Gabor (Sparse Representation-based Classification Based on Log-Gabor, Log-GSRC), which applies local features information of samples to the sparse representation method. Actually, SRC (Sparse Representation-based Classification) is using a linear correlation between the samples of one class which can be assumed that these samples exist in a subspace, and also can be linear represented with each other. It is a global representation and it ignores the local features information of the samples, while in the case of there are a smaller number of training samples per class, SRC will obtain an inaccurate classification result which may correspond to one and more classes in the process of sparse decomposition. However, the Log-GSRC combines global and local features information of the samples and also improves the robustness of SRC. The experimental results clearly showed that Log-GSRC has much better performance than SRC and also has much higher recognition rates than SRC in face recognition.
목차
1. Introduction
2. Sparse Representation-based Classification (SRC)
3. Gabor filters and Log-Gabor filters Theory
3.1. Gabor Filters
3.2. Log-Gabor Filters
3.3 Log-Gabor Features of Face Image
4. Sparse Representation-based Classification Based on Log-Gabor (Log-GSRC)
5. Experimental Results
5.1. ORL Database
5.2. AR Database
6. Conclusions
Acknowledgements
References