Recognizing facial images using ICA, LPP, MACE Gabor Filters, Score Level Fusion Techniques

We have developed and analyzed Independent Component Analysis (ICA), Locality Preserving Projections (LPP), Minimum Average Correlation Energy (MACE) Gabor Filters, Score Level Fusion Techniques (SLFT) for Face Recognition in the presence of various noises and blurring effects. ICA considers statistical characteristics in second order or higher order. LPP is used to generate an unsupervised neighborhood graph on training data, and then finds an optimal locality preserving projection matrix under certain criterion. MACE Gabor filter synthesizes a filter using a set of training images that would produce correlation output that minimizes correlation values at locations other than the origin and the value at the origin is constrained to a specific peak value. ICA, LPP, MACE Gabor Filter, SLFT are the 4 systems developed which were trained in the absence of noise, blurring effect but tested by imposing different levels of noises and blurring effects. To compare the performances six public face databases: IITK, ATT, JAFEE, CALTECH, GRIMACE, and SHEFFIELD are considered.