Face anti-spoofing is essential to prevent false facial verification by using a photo, video, mask, or a different substitute for an authorized person's face. Most of the state-of-the-art presentation attack detection (PAD) systems suffer from overfitting, where they achieve near-perfect scores on a single dataset but fail on a different dataset with more realistic data. This problem drives researchers to develop models that perform well under real-world conditions. This is an especially challenging problem for frame-based presentation attack detection systems that use convolutional neural networks (CNN). To this end, we propose a new PAD approach, which combines pixel-wise binary supervision with patch-based CNN. We believe that training a CNN with face patches allows the model to distinguish spoofs without learning background or dataset-specific traces. We tested the proposed method both on the standard benchmark datasets -- Replay-Mobile, OULU-NPU -- and on a real-world dataset. The proposed approach shows its superiority on challenging experimental setups. Namely, it achieves higher performance on OULU-NPU protocol 3, 4 and on inter-dataset real-world experiments.
翻译:使用照片、视频、蒙面罩或替代经授权的人的脸面部的不同替代品,防止假面部核查至关重要。 大多数最先进的演示攻击检测系统(PAD)都因安装过度而受损,在单一数据集上取得了近乎完美分数,但在使用更现实的数据的不同数据集上却失败。 这个问题促使研究人员开发在现实世界条件下运行良好的模型。 对于使用动态神经网络(CNN)的基于框架的演示攻击探测系统来说,这是一个特别具有挑战性的问题。 为此,我们建议采用一种新的PAD方法,将像素明智的二进制监督与基于补丁的CNN(PAD)相结合。 我们相信,对有面部的CNNC(P)系统进行培训可以让模型在不学习背景或数据集特定痕迹的情况下区分 spoofs 。 我们在标准基准数据集 -- -- Replay-Mobile, OULU-NPU -- NPU -- -- 和真实世界数据集上测试了拟议方法。拟议的方法显示了其在挑战性实验组合上具有优势。 也就是说,它在4个世界间卫星协议上实现了更高的表现。