A comparative study on ICA and LPP based Face Recognition under varying illuminations and facial expressions

Dimensionality reduction has been a key problem in Face Recognition. Independent Component Analysis (ICA) is a recent approach for dimensionality reduction. Locality Preserving Projections (LPP) is also a recently proposed new method in pattern recognition for feature extraction and dimension reduction. In this paper we have developed and analyzed the face recognition rate of ICA and LPP under varying illuminations and facial expressions. Analyzes is performed on YALEB databases which contains 64 illuminations conditions (5760 images) and ATT databases which contains major facial expressions (400 images). From the results we conclude that the best algorithm to recognize images with varying illuminations is ICA. On the other hand to recognize image with varying facial expressions, LPP is better to use because it has better recognition rate.