论文标题
跨域面部表现攻击检测通过多域分段表示学习
Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning
论文作者
论文摘要
面部表现攻击检测（PAD）是在面部识别系统中解决的紧迫问题。常规方法通常假定测试和培训在同一领域内；结果，它们可能无法很好地概括为看不见的场景，因为在培训集中学到的垫子的表示形式可能会过分地适合对象。鉴于此，我们提出了一个有效的跨域脸部填充图表学习。我们的方法包括分解的表示学习（DR-NET）和多域学习（MD-NET）。 Dr-net通过生成模型学习了一对编码器，这些模型可以将pad pad信息从主题歧视性特征中删除。来自不同域的分离功能被馈送到MD-NET，该功能学习了最终跨域面垫任务的独立于域的功能。在几个公共数据集上进行的广泛实验验证了跨域垫的拟议方法的有效性。
Face presentation attack detection (PAD) has been an urgent problem to be solved in the face recognition systems. Conventional approaches usually assume the testing and training are within the same domain; as a result, they may not generalize well into unseen scenarios because the representations learned for PAD may overfit to the subjects in the training set. In light of this, we propose an efficient disentangled representation learning for cross-domain face PAD. Our approach consists of disentangled representation learning (DR-Net) and multi-domain learning (MD-Net). DR-Net learns a pair of encoders via generative models that can disentangle PAD informative features from subject discriminative features. The disentangled features from different domains are fed to MD-Net which learns domain-independent features for the final cross-domain face PAD task. Extensive experiments on several public datasets validate the effectiveness of the proposed approach for cross-domain PAD.
