论文标题
使用带有面部和语音识别的合奏分类器对生物识别用户身份验证的评估
Evaluation of biometric user authentication using an ensemble classifier with face and voice recognition
论文作者
论文摘要
本文基于采用面部和语音识别分类器的集合设计的生物识别用户身份验证系统。设计方法需要对各个分类器进行开发和性能评估，以进行面部和语音识别以及随后在整体框架内整合两者。绩效评估采用了三个基准数据集，这些数据集是NIST FERET脸，耶鲁大学延伸面和ElsDSR语音。三个基准数据集上集合设计的绩效评估表明，双峰身份验证系统在准确性，精度，真实负率和真正的正率指标上提供了显着改进，同时产生最小的假正和负率低于1％。
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%.
