Evaluation of biometric user authentication using an ensemble classifier with face and voice recognition
This work addresses security and user verification problems for applications requiring reliable authentication, but it is incremental as it combines existing methods.
The paper tackled biometric user authentication by developing an ensemble system combining face and voice recognition, achieving accuracy and precision metrics at or above 99% with false positive and negative rates below 1% on benchmark datasets.
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%.