A Novel Adaptive Inertia Particle Swarm Optimization (AIPSO) Algorithm for Improving Multimodal Biometric Recognition

In this paper, we present an efficient feature selection scheme for biometric authentication (for both unimodal and multimodal systems) that allows selecting the dominant features and increase the performance of the overall system. More precisely, we propose an Adaptive Inertia Particle Swarm Optimization (AIPSO) algorithm such that the particle inertia weights are iteratively updated according to the particle fitness value. We then use AIPSO for selecting Log Gabor features for the face and palmprint modalities independently and on the fused Log Gabor space of these two modalities considered for fusion. Final classification (in both schemes) is performed on the projection space of the selected features using Kernel Direct Discriminant Analysis (KDDA). Extensive experiments are carried out on 250 users selected from FRGC face database, PolyU palmprint database and a virtual person multimodal biometric database built from the considered face and palmprint databases. We compare the proposed selection method with well known feature selection schemes such as Sequential Floating Forward Selection (SFFS), Genetic Algorithm (GA), Adaptive Boosting (AdaBoost) and Normal PSO in terms of both number of features selected and performance. Experimental result results show better performance of our AIPSO compared to all other techniques with an improvement of around 5% in performance and a reduction of around 62% of features compared to the initial system (with full features).