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This paper presents a biometric user authentication system based on anensemble design that employs face and voice recognition classifiers. The designapproach entails development and performance evaluation of individualclassifiers for face and voice recognition and subsequent integration of thetwo within an ensemble framework. Performance evaluation employed threebenchmark datasets, which are NIST Feret face, Yale Extended face, and ELSDSRvoice. Performance evaluation of the ensemble design on the three benchmarkdatasets indicates that the bimodal authentication system offers significantimprovements for accuracy, precision, true negative rate, and true positiverate metrics at or above 99% while generating minimal false positive andnegative rates of less than 1%.

Evaluation of biometric user authentication using an ensemble classifier with face and voice recognition