Real-time face recognition system using radial basis function neural networks

Aziz, Khairul Azha A (2011) Real-time face recognition system using radial basis function neural networks. Project Report. Universiti Teknikal Malaysia Melaka. (Submitted)

Text (24 Pages) Real-time face recognition system using radial basis function neural networks (2).pdf - Submitted Version Download (3MB)

Abstract

Face detection is the first step in face recognition system. The purpose is to localize and extract the face region from the background that will be fed into the face recognition system for identification. This project used general preprocessing approach for normalizing the image and Radial Basis Function (RBF) Neural Network will be used for face recognition. As for the detection process, new reduced set method for Support Vector Machines is used. The image will then be resized converted into grayscale format. The RBF network is trained using the user image and tested using several faces. Radial Basis Function (RBF) Neural Network was used to distinguish between the user face and imposter. RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using fast two stages training algorithm and the network possesses the property of best approximation. The performance of the RBFNN face recognition system will be based on the False Acceptance Rate (FAR) and the False Rejection Rate (FRR).

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