Design a Biometric Identification System Based on the Fusion of Hand Geometry and Backhand Patterns

Abstract

This paper describes the design and development of a prototype system for the automatic identification of an individual based on the fusion of hand geometry with backhand patterns. Information fusion at the feature extraction and at the confidence level, where the matching scores reported by Bayesian backpropagation neural network, is discussed. The system was tested with the template files. The test performance, False Acceptance Rate (FAR) = 10% and False Rejection Rate (FRR) = 0%, suggests that the system can be used in medium/high security large buildings environments.