PSO Versus AdaBoost for Feature Selection in Multimodal Biometrics

Date Added: Jun 2009
Format: PDF

In this paper, the authors present an efficient feature level fusion scheme that they apply on face and palmprint images. The features for each modality are obtained using Log Gabor transform and concatenated to form a fused feature vector. The authors then use Particle Swarm Optimization (PSO) scheme to reduce the dimension of this vector. Final classification is performed on the projection space of the selected features using Kernel Direct Discriminant Analysis (KDDA). Extensive experiments are carried out on a virtual multimodal biometric database of 250 users built from the face FRGC and the palmprint PolyU databases.