The nature of statistical learning theory
The nature of statistical learning theory
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Face recognition based on kernelized extreme learning machine
AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Incremental face recognition: hybrid approach using short-term memory and long-term memory
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
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Support Vector Machine (SVM) provides high performance in generalization, processing small samples, and tackling high-dimensional data. Based on the advantages of SVM, an approach is proposed in this paper, adopting multi-class SVM to realize face recognition. In the approach, Principle Component Analysis (PCA) is used firstly to reduce dimensions so that feature extraction is carried out on face images. Then a method based on One-Versus-All SVM is implemented to realize multi-class classification on feature vectors of the face images. Results of experiments applied to ORL and Yale face databases show that our approach is effective. By the One-Versus-All SVM method, we can respectively obtain recognition rates as high as 93.5% in ORL face database, and 97.3% in Yale face database.