Principal Component Analysis and Fisher Linear Discriminant Analysis

Principal Components Analysis (PCA) is an appearance based technique used widely for the dimensionality reduction and it records a great performance in face recognition. PCA based approaches typically include two phases: training and classification (Draper et al 2003). In the training phase, an Eigen space is established from the training samples using PCA and the training face images are mapped to the Eigen space for classification. In the classification phase, an input face is projected to the same Eigen space and is classified by an appropriate classifier.