Hybrid PSO Based Integration of Multiple Representations of Thermal Hand Vein Patterns

This paper outlines a novel personal authentication approach by integrating the multiple feature representations of thermal hand vein patterns. In the present work, vein patterns are regarded as comprising textures. Accordingly two types of texture features using Gabor wavelets and fuzzy logic are extracted from the acquired vein images. Since both the approaches have different domains of feature representation, their integration is accomplished at the decision level by incorporating individual decisions using the Euclidean distance based classifiers. The optimal decision parameters comprising individual decision thresholds and one fusion rule out of 16 rules for two features are estimated with the help of hybrid Particle Swarm Optimization (PSO) which can optimize the decisions taken by the individual classifiers. The experimental results carried out on 100 user database are promising thus confirming the usefulness of the proposed authentication system.