This paper presents a biometric user authentication system based on an
ensemble design that employs face and voice recognition classifiers. The design
approach entails development and performance evaluation of individual
classifiers for face and voice recognition and subsequent integration of the
two within an ensemble framework. Performance evaluation employed three
benchmark datasets, which are NIST Feret face, Yale Extended face, and ELSDSR
voice. Performance evaluation of the ensemble design on the three benchmark
datasets indicates that the bimodal authentication system offers significant
improvements for accuracy, precision, true negative rate, and true positive
rate metrics at or above 99% while generating minimal false positive and
negative rates of less than 1%.
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