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In this paper, a new type of hybrid method that hybridizes PCA and EBGM as a two-stage procedure is presented to improve recognition performance in large-scale face recognition. Among various methods in face recognition, PCA is considered to identify human faces by holistic views, while EBGM is supposed to distinguish one face from another by details, but they are both excellent representative methods...
This paper presents a parallel method for EBGM face recognition. Compared with other methods such as principal component analysis (PCA) and linear discriminant analysis (LDA), EBGM has the advantage of higher accuracy, however, with more computational time and memory usage, which also mean less practicability. We propose a parallel method for EBGM by balancing the unit of images. We distribute the...
Face recognition has received a lot of attention in biometrics and computer vision. A lot of face recognition algorithms have been developed during the past decades. This paper reviews three classical methods Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Elastic Bunch Graph Matching (EBGM). Three algorithms are implemented with Matlab. The algorithm performance is evaluated...
Multicategory support vector machines (MC-SVM) are powerful classification systems with excellent performance in a variety of biological classification problems. However, the process of generating models in traditional multicategory support vector machines is very time-consuming, especially for large datasets. In this paper, parallel multicategory support vector machines (PMC-SVM) have been developed...
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