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A Fingerprint and Voiceprint Fusion Identity Authentication Method

机译:指纹与声纹融合身份认证方法

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In the traditional biometric identification scheme, the single fingerprint feature points are used for identification, or the single voiceprint is used as the authentication standard, and it is difficult to obtain a good accuracy in a complicated environment. Different biometrics have different advantages, disadvantages and applicable scenarios. A single mode cannot have a wider coverage scene. For this reason, we propose a fusion algorithm for fingerprint recognition and voiceprint recognition, combining the recognition characteristics of fingerprint and voiceprint, an identification scheme based on fingerprint and voiceprint fusion is proposed. The eigenvalues of fingerprint and voiceprint are divided into a group. The depth neural network is used to extract the fingerprint and voiceprint features respectively, and the probability combination is used to verify the fusion. The experimental results show that the combination of the two certifications reduces the error acceptance rate (FAR) by 4.04% and the error rejection rate (FRR) by 1.54% compared to a single fingerprint or voice-print recognition scheme.
机译:在传统的生物特征识别方案中,使用单个指纹特征点进行识别,或者使用单个声纹作为认证标准,在复杂的环境中很难获得良好的精度。不同的生物识别技术具有不同的优势,劣势和适用场景。单一模式不能具有更广的覆盖范围。为此,我们提出了一种指纹识别和声纹识别的融合算法,结合指纹和声纹的识别特性,提出了一种基于指纹和声纹融合的识别方案。指纹和声纹的特征值分为一组。深度神经网络分别用于提取指纹和声纹特征,概率组合用于验证融合。实验结果表明,与单个指纹或声纹识别方案相比,两种认证的组合可将错误接受率(FAR)降低4.04%,将错误拒绝率(FRR)降低1.54%。

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