Bimodal biometric person authentication using speech and face under degraded condition

In this work, we present a bimodal biometric system using speech and face features and tested its performance under degraded condition. Speaker verification (SV) system is built using Mel-Frequency Cepstral Coefficients (MFCC) followed by delta and delta-delta for feature extraction and Gaussian Mixture Model (GMM) for modeling. A face verification (FV) system is built using the combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Sum rule is used for the fusion of the biometric scores. The performance of SV system under degraded condition is also checked. All the experimental results are shown upon a subset of IITG-DIT M4 multi-biometric database. The complementary information derived from the speech biometric at training stage is used to further decrease the FV error rate, which is termed as Cohort fed FV system. Finally we propose an improved bimodal person authentication system using SV and Cohort fed FV biometric systems.