FACE RECOGNITION WITH NOVEL SELF ORGANIZING MAP USING NEURAL NETWORK

Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult. In recent years, face recognition has received attention from both research communities and the market. A lot of face recognition algorithms, along with their medications, have been developed during the past decades. A number of typical algorithms are presented. In this paper, we propose to label a Self-Organizing Map to measure image similarity. To manage this goal, we feed facial images associated to regions of interest into the neural network. At the end of learning step, each neural unit is tuned to a particular facial image prototype. The Self-Organizing Map provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and in variance to minor changes in the image sample. This paper presents a novel Self-Organizing Map for face recognition. The SOM method is trained on images from one database. A face Recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the way is to do this is by comparing selected facial features from the image and a facial database. It is typically used in security systems and also compared to other biometrics like fingerprint or eye iris recognition systems