Face presentation attack detection (PAD) plays a pivotal role in securing face recognition systems against spoofing attacks. Although great progress has been made in designing face PAD methods, developing a model that can generalize well to an unseen test domain remains a significant challenge. Moreover, due to different types of spoofing attacks, creating a dataset with a sufficient number of samples for training deep neural networks is a laborious task. This work addresses these challenges by creating synthetic data and introducing a deep learning-based unified framework for improving the generalization ability of the face PAD. In particular, synthetic data is generated by proposing a video distillation technique that blends a spatiotemporal warped image with a still image based on alpha compositing. Since the proposed synthetic samples can be generated by increasing different alpha weights, we train multiple classifiers by taking the advantage of a specific type of ensemble learning known as a stacked ensemble, where each such classifier becomes an expert in its own domain but a non-expert to others. Motivated by this, a meta-classifier is employed to learn from these experts collaboratively so that when developing an ensemble, they can leverage complementary information from each other to better tackle or be more useful for an unseen target domain. Experimental results using half total error rates (HTERs) on four PAD databases CASIA-MFSD (6.97 %), Replay-Attack (33.49%), MSU-MFSD (4.02%), and OULU-NPU (10.91%)) demonstrate the robustness of the method and open up new possibilities for advancing presentation attack detection using ensemble learning with large-scale synthetic data.
翻译:脸部突袭检测(PAD)在确保面部识别系统不受嘲笑攻击方面发挥着关键作用。尽管在设计面部PAD方法方面取得了巨大进展,但开发一个模型,能够向无形的测试领域推广,仍是一个重大挑战。此外,由于不同类型的表面攻击,创建数据集,为深神经网络培训提供足够数量的样本,这是一项艰巨的任务。这项工作通过创建合成数据和引入一个基于深层次学习的统一框架来应对这些挑战,以提高脸部PAD的普及能力。特别是,合成数据是通过提出一种视频蒸馏技术来生成的,该技术可以将一个波形瞬间扭曲的图像与基于阿尔法复映的静止图像混为一体。由于拟议合成样本可以通过增加不同的字母重量生成,因此我们利用一种特定类型的全方位学习,称为堆叠式堆放。 每一个这样的分类都成为本领域的专家专家,但非专家。为此,将一个元级SDSDSDSD(O) 升级,将一个图像转换成一个基于直径直径的图像图像图像与一个半域域域域域域,在开发一个更好的分析结果时,(RADMFMFA) 。(RMF) 4) 。可以从这些元级中学习一个更好的工具, 。使用另一个工具,可以使用另一个的 Reql) 。