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Multi-biometrics fusion (heart sound-speech authentication system)

机译:多种生物特征融合(心音认证系统)

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摘要

Biometrics recognition systems implemented in a realworld environment often have to be content with adverse biometrics signal acquisition which can vary greatly in this environment. This includes acoustic noise that can contaminate speech signals or artifacts that can alter heart sound signals. In order to overcome these recognition errors, researchers all over the world apply various methods such as normalization, feature extraction, classification to address this issue. Recently, combining biometrics modalities has proven to be an effective strategy to improve the performance of biometrics systems. The approach in this paper is based on biometrics recognition which used the heart sound signal as a feature that can't be easily copied The Mel- Frequency Cepstral Coefficient (MFCC) is used as a feature vector and vector quantization (VQ) is used as the matching model algorithm. A simple yet highly reliable method is introduced for biometric applications. Experimental results show that the recognition rate of the Heart Sound Speaker identification (HS-SI) model is 81.9% while (S-SI) the rate for the Speech Speaker Independent model is 99.3% for a 21 client, 40 imposter database. Heart sound- speaker verification (HS-SV) provides an average EER of 17.8% while the average EER for the speech speaker verification model (S-SV) is 3.39%. In order to reach a higher security level an alternative to the above approach, which is based on multimodal and a fusion technique, is implemented into the system. The best performance of the work is based on simple-sum score fusion with a pricewise-linear normalization technique which provides an EER of 0.69%.
机译:在现实环境中实现的生物特征识别系统通常必须满足不利的生物特征信号采集的要求,在这种环境下,生物信号识别可能会发生很大变化。这包括可能会污染语音信号的噪声或可能会更改心音信号的伪影。为了克服这些识别错误,全世界的研究人员都采用了各种方法,例如归一化,特征提取,分类来解决这个问题。最近,事实证明,结合生物特征识别方法是提高生物特征识别系统性能的有效策略。本文中的方法基于生物特征识别,该特征使用心音信号作为不容易复制的特征。将Mel-频率倒谱系数(MFCC)用作特征向量,并将矢量量化(VQ)用作特征向量。匹配模型算法。引入了一种简单而高度可靠的方法用于生物识别应用。实验结果表明,对于21位客户,40个冒名顶替者数据库,心音说话者识别(HS-SI)模型的识别率为81.9%,而(S-SI)语音说话者独立模型的识别率为99.3%。心音说话人验证(HS-SV)提供的平均EER为17.8%,而语音说话人验证模型(S-SV)的平均EER为3.39%。为了达到更高的安全级别,在系统中实现了基于多模式和融合技术的上述方法的替代方案。作品的最佳性能基于简单和分数融合以及价格线性均衡技术,可提供0.69%的EER。

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