A Novel Multi-modal Biometric Architecture for High-Dimensional Features

Abstract

Dealing with high-dimensional data has an important role in a number of areas, including biometric recognition in both real world and emerging virtual reality applications. Acquiring a group of different biometrics with various characteristics and specifications results in a number of issues that should be addressed, while developing such multi-modal recognition system. In this paper, we propose a novel Multi-Modal Biometric System based on neural network paradigm which utilizes the ear and face features and has unique method to train different classifiers based on each feature set. The aggregation result depicts the final decision over the recognized identity. In order to train accurate set of classifiers, the subspace clustering method has been used to overcome the problem of high dimensionality of the feature space. The proposed system is based on a new methodology for shrinking down the finite search space of all possible subspaces by focusing on axis-parallel subspaces which is a novel approach in data clustering for biometric dataset. The experimental results over the FERET dataset show the superiority of the proposed method over several dimensionality reduction methods.