Face Recognition using PCA and LDA with Singular Value Decomposition (SVD)

Linear Discriminant Analysis(LDA) is well-known scheme for feature extraction and dimension reduction. It has been used widely in many applications involving high-dimensional data, such as face recognition. In this paper we present a new variant on Linear Discriminant Analysis (LDA) for face recognition by reducing dimensions of input data using matrix representation and after that using singular value decomposition to reduce dimensions of scatter matrix. Experiments on ORL face database shows the effectiveness of our proposed algorithm and results compared with other LDA based methods shows that the proposed scheme gives comparatively better results than previous methods in terms of recognition rate and reduced time complexity.

• Type: Conference
• Year: 2008
• Where: IPCV
• Authors: Neeta Nain, Nitish Agarwal, Prashant Gour, Rakesh P. Talawar, Subhash Chandra
• Keywords: Face Recognition, Image Processing, IPCV 2008, Linear Discriminant, Linear Discriminant Analysis