Face recognition based on 2DLDA and support vector machine
Junying Gan, Si-Bin He · 2009
Singularity problem of LDA algorithm is overcome by Two-dimensional LDA(2DLDA), and Support Vector Machine(SVM) has the character of Structural Risk Minimization. In this paper, two methods are combined and used for face recognition. Firstly, the original images are decomposed into high-frequency and low-frequency components with the help of Wavelet Transform(WT). The high-frequency components are ignored, while the low-frequency components can be obtained. Then, the liner discriminant features are extracted by 2DLDA, and SVM is selected to perform face recognition. Experimental results based on ORL(Olivetti Research Laboratory) and Yale face database show the validity of 2DLDA+SVM for face recognition.