Multi-modal identity verification using support vector machines (SVM)

Abstract

The contribution of this paper is twofold: (1) to formulate a decision fusion problem that is encountered in the design of a multi-modal identity verification system as a particular classification problem, and (2) to solve this problem by using a support vector machine (SVM). The multi-modal identity verification system under consideration is built of d modalities in parallel, each one delivering as output a scalar number, called a score, stating how well the claimed identity is verified. A fusion module receiving the d scores as input has to take a binary decision: to accept or reject the identity. This fusion problem has been solved using SVMs. The performance of this fusion module has been evaluated and compared with other proposed methods on a multi-modal database containing both vocal and visual modalities.