Person verification using multimodal biometric system

Abstract

This paper presents a multimodal approach for person verification based on the features extracted from signature, face and iris of an individual. Features from signatures are extracted using Discrete Cosine Transform (DCT) and by applying Sparse Representation techniques. Facial features are extracted using Gabor Filter bank and Kernel Principal Component Analysis (KPCA). In this work, for extracting features from iris, we proposed Gabor filter bank and KPCA. The feature vectors so obtained are then given as input to classifiers. Support Vector Machines (SVM) classifiers are used for the three modalities. The final decision of multimodal system is based on the majority voting of classifiers. The SVM classifiers are trained and tested using the following databases — SUSIG-Signature, ORL-Face and UBIRIS — Iris. The experimental analysis shows that the performance of multimodal system has attained a GAR of 99.5% at an FAR of 0%.