An Application of Face Recognition System using Image Processing and Neural Networks

In recent years face recognition has received substantial attention from both research communities and the market, but still remained very challenging in real applications. 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 (SOM) to measure image similarity. To manage this goal, we feed Facial images associated to the regions of interest into the neural network. At the end of the learning step, each neural unit is tuned to a particular Facial image prototype. Facial recognition is then performed by a probabilistic decision rule. This scheme offers very promising results for face identification dealing with illumination variation and facial poses and expressions. This paper presents a novel SelfOrganizing Map (SOM) for face recognition. The SOM method is trained on images from one database. The novelty of this work comes from the integration of A facial 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 can be compared to other biometrics such as fingerprint or eye iris recognition systems.