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A Fingerprint and Voiceprint Fusion Identity Authentication Method

机译:指纹和声纹融合标识身份验证方法

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摘要

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:在传统的生物识别方案中,单个指纹特征点用于识别,或者单个VoicePrint用作认证标准,并且难以在复杂的环境中获得良好的精度。不同的生物识别技术具有不同的优缺点和适用的情景。单个模式不能具有更宽的覆盖场景。因此,提出了一种用于指纹识别和声纹识别的融合算法,组合指纹和声道的识别特性,提出了一种基于指纹和探测融合的识别方案。指纹和声音的特征值分为一个组。深度神经网络分别用于分别提取指纹和声纹特征,并且概率组合用于验证融合。实验结果表明,与单个指纹或语音打印识别方案相比,两项认证的组合将误差接收率(远)降低了4.04%,误码抑制率(FRR)减少了1.54%。

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