Robust Face Recognition in Low Dimensional Subspace Using Reconstructive and Discriminative Features

In this paper, we have discussed dimensionality reduction techniques for face recognition - Principle Component Analysis (PCA) and Fisher Discriminant Analysis(FDA). Both the methods are based on linear projection, which projects the face from higher dimensional image space to lower dimensional feature space. PCA derives the most expressive features (MEF) by projecting face vector such that it captures greatest variance. FDA derives most discriminating features(MDF) by maximizing between class scatter and minimizing within class scatter. Lower dimensional features are used for recognition process. Classification can be achieved using Neural Network (NN), Support Vector Machine (SVM) etc. We have tested our system for the L2 norm measure. At the end of the paper, we have discussed results which show that FDA out weights the performance of PCA with average recognition rate more than 95%.