Fusion Strategies for Multimodal Biometric System Using Face and Voice Cues

Abstract

Deployment of biometric systems in the applications of real world includes the most of unimodal biometric systems. Unimodal biometric system based on the information collected from single source. Sometimes single source of information may not identify the individual correctly because of some limitations such as Non-universality, Noisy data, Intra-class variation, Spoof attacks and Intra-class similarities. Various limitations of unimodal biometric systems are overridden by the multimodal biometric systems which involves multiple sources of information. Multimodal systems can be constructed by fusing of information of multiple modalities. This fusion can take place at various steps of processing such as at image acquisition, extraction of features of the traits, matching of test vectors with trained vectors and during decision taking based on classification. This paper presents a system of multimodal biometrics using face and voice biometric traits by including four fusion methods. Fusion takes place at i) feature level using concatenation of face and voice features, ii) score level using method involving the maximum of mode of scores obtained from two matchers, iii) rank level using borda count & iv) decision level fusion using logical conjunction (AND). Fusing of Log Gabor & Local Binary Pattern (LBP) takes place at the facial feature extraction. The voice features are also fused using Mel Frequency Ceptral coefficients (MFCCs) and Linear Predictive Coefficient features (LPC). Computation of similarity between test feature vectors and training vectors is carried out using Euclidian distance during matching process. KNN Classifier is used during decision making. Performance evaluation of these techniques are also carried out using performance measures such as Accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR) and ROC curves.