论文标题
通过多任务学习和一侧元三重损失，通用的面部反欺骗
Generalized Face Anti-Spoofing via Multi-Task Learning and One-Side Meta Triplet Loss
论文作者
论文摘要
随着面部表现攻击的越来越多的变化，模型的概括成为实用面部反欺骗系统的基本挑战。本文介绍了一个广义的反企业框架，其中包括三个任务：深度估计，面部解析和实时/欺骗分类。通过从面部解析和深度估计任务进行像素的监督，正则化功能可以更好地区分欺骗面孔。在使用元学习技术模拟结构域移动的同时，提出的一侧三重态损耗可以进一步提高概括能力。在四个公共数据集上进行的广泛实验表明，所提出的框架和培训策略比以前的模型概括更有效，可以看不见。
With the increasing variations of face presentation attacks, model generalization becomes an essential challenge for a practical face anti-spoofing system. This paper presents a generalized face anti-spoofing framework that consists of three tasks: depth estimation, face parsing, and live/spoof classification. With the pixel-wise supervision from the face parsing and depth estimation tasks, the regularized features can better distinguish spoof faces. While simulating domain shift with meta-learning techniques, the proposed one-side triplet loss can further improve the generalization capability by a large margin. Extensive experiments on four public datasets demonstrate that the proposed framework and training strategies are more effective than previous works for model generalization to unseen domains.
