Multimodal biometric person authentication system using speech and signature features

In this work, we present a multimodal biometric system using speech and signature features. Speaker recognition system is built using Mel frequency cepstral coefficients (MFCC) for feature extraction and vector quantization (VQ) for modeling. An offline signature recognition system is also built using vertical and horizontal projection profiles (VPP and HPP) and discrete cosine transform (DCT) for feature extraction. A multimodal biometric database with speech and signature biometric features collected from 30 users is used for the study. A multimodal biometric system is demonstrated using score level fusion of speaker and signature recognition systems. Sum rule is used for the fusion of the biometric scores. Experimental results show the efficacy of multimodal biometric system using speech and signature features when the biometric data is affected by noise.