论文标题
一种新型的面部抗欺骗神经网络模型，用于面部识别和检测
A Novel Face-Anti Spoofing Neural Network Model For Face Recognition And Detection
论文作者
论文摘要
面部识别（FR）系统已用于各种应用，包括道路交叉，银行业和移动银行业务。 FR系统的广泛使用引起了人们对面部生物识别技术免受欺骗攻击的安全性的担忧，这些攻击使用合法用户的面孔的照片或视频来获得对资源或活动的非法访问。尽管开发了几种FAS或LIVISE检测方法（这些方法是在获取时面部是否存在或欺骗一张面孔），但由于难以识别歧视和价格合理的欺骗特征，但也无法解决问题，但问题仍然无法解决。此外，某些面部部分经常与图像混乱相关或相关，从而导致整体性能差。这项研究提出了一个面对抗刺激的神经网络模型，该模型胜过现有模型，效率为0.89％。
Face Recognition (FR) systems are being used in a variety of applications, including road crossings, banking, and mobile banking. The widespread use of FR systems has raised concerns about the safety of face biometrics against spoofing attacks, which use the use of a photo or video of a legitimate user's face to gain illegal access to the resources or activities. Despite the development of several FAS or liveness detection methods (which determine whether a face is live or spoofed at the time of acquisition), the problem remains unsolved due to the difficulty of identifying discrimination and operationally reasonably priced spoof characteristics but also approaches. Additionally, certain facial portions are frequently repeated or correlate to image clutter, resulting in poor performance overall. This research proposes a face-anti-spoofing neural network model that outperforms existing models and has an efficiency of 0.89 percent.
