An improved biometric identification system based on heart sounds and Gaussian Mixture Models

F. Beritelli, Andrea Spadaccini
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引用次数: 19

Abstract

This paper presents an evolution of a biometric identity verification system based on heart sounds. The system is built using Gaussian Mixture Models (GMMs) and uses features extracted both from the spectral domain and the time domain in order to improve the performance, measured in terms of Equal Error Rate (EER), with respect to similar systems. The best result obtained using our approach, computed over a database of 165 people, is an EER of 13,70 %, that outperforms other similar approaches.
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一种改进的基于心音和高斯混合模型的生物识别系统
本文介绍了一种基于心音的生物识别身份验证系统的发展。该系统使用高斯混合模型(GMMs)构建,并使用从谱域和时域提取的特征,以等效错误率(EER)衡量,相对于类似系统,以提高性能。使用我们的方法获得的最佳结果是,在165人的数据库中计算,EER为13.70%,优于其他类似方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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