人脸识别与深度学习反欺骗识别

None Fathima Jameera. B, None G. Suresh, None S. Hemalatha, None S. Vilma Veronica
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引用次数: 0

摘要

这项研究工作将尖端的反欺骗技术与深度学习方法相结合,以解决面部识别系统的欺骗攻击问题。该项目的第一步是编译包含真实面部照片和几种欺骗攻击场景的多样化数据集。然后,采用数据预处理方法保证数据一致性和最佳模型性能。本研究利用MobileNet和VGG-16这两种知名的深度学习架构,构建可靠的面部识别模型。彻底的评估使用完善的指标,包括分类报告,准确性分数,和混淆矩阵进行彻底的培训和验证后。这项研究意义重大,因为它结合了实时反欺骗能力,超越了传统的面部识别工作。网络摄像头功能被添加到部署的模型中,以评估与参考护照大小的照片相比的实时图像。动态变化的边界框颜色——真实人脸为蓝色,检测到的假图像为红色——表明了抗欺骗技术。该项目的结论包含了对MobileNet和VGG-16模型的全面比较,确定并比较了每种模型的优缺点。实时演示也突出了反欺骗方法在实践中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
People Identification Through Facial Recognition and Anti-Spoofing Using Deep Learning
This research effort uses cutting-edge anti-spoofing techniques in conjunction with deep learning approaches to address the issue of spoofing assaults on facial recognition systems. A diversified dataset containing real facial photos and several spoofing attack scenarios is compiled as the project's first step. Then, data pretreatment methods are used to guarantee data consistency and the best model performance. The research makes use of MobileNet and VGG-16, two well-known deep-learning architectures, to build reliable facial recognition models. A thorough evaluation using well-established metrics including classification reports, accuracy scores, and confusion matrices is undertaken after thorough training and validation. It's significant because this research incorporates real-time anti-spoofing capabilities, which go beyond traditional facial recognition jobs. Webcam functionality is added to the deployed models to assess real-time images in comparison to reference passport-size photos. Dynamically shifting boundary box colors—blue for real faces and red for detected fake images—indicate the anti-spoofing technology. The project's conclusion contains a thorough comparison of the MobileNet and VGG-16 models that identifies and compares each model's advantages and disadvantages. Real-time demos also highlight the anti-spoofing methodology's effectiveness in practice.
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