Title :
Application of Radial Basis Function Network and Locality Preserving Projections for Face Recognition
Author :
Mei, Jian-qiang ; Liu, Zheng-guang ; Ming, Ming
Author_Institution :
Tianjin Univ., Tianjin
Abstract :
Locality preserving projections (LPP) is a linear method that optimally preserves the local structure of the data set. However, because of the continuing existence of transformation difference, LPP subspace is failed to detect the important nonlinear variations of the face manifold. In order to improve the recognition performance, we propose to use radial basis function network (RBFN) to classify the features in the LPP subspace. The multi-quadrics function is taken as the activation function of the RBFN and the hidden layer of RBFN is trained via the gradient descent algorithm in our approach. The experimental results show that the LPP+RBFN method has achieved a better rate than Laplacianfaces, Fisherfaces and Eigenfaces.
Keywords :
face recognition; gradient methods; image classification; radial basis function networks; transfer functions; activation function; face manifold; face recognition; feature classification; gradient descent algorithm; linear method; local structure preservation; locality preserving projections; multiquadrics function; radial basis function network; transformation difference; Application software; Automation; Face detection; Face recognition; Laplace equations; Linear discriminant analysis; Mean square error methods; Multi-layer neural network; Principal component analysis; Radial basis function networks;
Conference_Titel :
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2875-5
DOI :
10.1109/ICNC.2007.262