Face recognition using multiple facial features
We propose a face recognition method that fuses information acquired from global and local features of the face for improving performance. Principle components analysis followed by Fisher analysis is used for dimensionality reduction and construction of individual feature spaces. Recognition is done by probabilistically fusing the confidence weights derived from each feature space. The performance of the method is validated on FERET and AR databases.
face recognition principal components analysis fishers linear discriminant block histogram modification edginess image distance in feature space fusion