Alharbi Bayan, Alshanbari Hanan S
Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia.
PeerJ Comput Sci. 2023 Jul 11;9:e1468. doi: 10.7717/peerj-cs.1468. eCollection 2023.
Information security has become an inseparable aspect of the field of information technology as a result of advancements in the industry. Authentication is crucial when it comes to dealing with security. A user must be identified using biometrics based on certain physiological and behavioral markers. To validate or establish the identification of an individual requesting their services, a variety of systems require trustworthy personal recognition schemes. The goal of such systems is to ensure that the offered services are only accessible by authorized users and not by others. This case study provides enhanced accuracy for multimodal biometric authentication based on voice and face hence, reducing the equal error rate. The proposed scheme utilizes the Gaussian mixture model for voice recognition, FaceNet model for face recognition and score level fusion to determine the identity of the user. The results reveal that the proposed scheme has the lowest equal error rate in comparison to the previous work.
随着信息技术行业的发展，信息安全已成为该领域不可或缺的一部分。在处理安全问题时，身份验证至关重要。必须基于某些生理和行为特征，使用生物识别技术来识别用户。为了验证或确定请求其服务的个人身份，各种系统都需要可靠的个人识别方案。此类系统的目标是确保所提供的服务仅可供授权用户访问，而其他用户无法访问。本案例研究提高了基于语音和面部的多模态生物识别认证的准确性，从而降低了误识率。所提出的方案利用高斯混合模型进行语音识别，使用FaceNet模型进行面部识别，并通过分数级融合来确定用户身份。结果表明，与先前的工作相比，所提出的方案具有最低的误识率。
