Face anti-spoofing is crucial for the security of face recognition systems. Learning based methods especially deep learning based methods need large-scale training samples to reduce overfitting. However, acquiring spoof data is very expensive since the live faces should be re-printed and re-captured in many views. In this paper, we present a method to synthesize virtual spoof data in 3D space to alleviate this problem. Specifically, we consider a printed photo as a flat surface and mesh it into a 3D object, which is then randomly bent and rotated in 3D space. Afterward, the transformed 3D photo is rendered through perspective projection as a virtual sample. The synthetic virtual samples can significantly boost the anti-spoofing performance when combined with a proposed data balancing strategy. Our promising results open up new possibilities for advancing face anti-spoofing using cheap and large-scale synthetic data.
翻译:以学习为基础的方法,特别是深层学习方法,需要大规模培训样本,以减少过度装配。然而,获取假数据非常昂贵,因为活面孔应该重新打印,并在许多观点中重新捕捉。在本文中,我们提出了一个在3D空间合成虚拟假面孔数据的方法,以缓解这一问题。具体地说,我们认为印刷照片是一个平坦的表面,将它嵌入一个3D对象,然后在3D空间随机弯曲和旋转。随后,变换的3D照片通过虚拟样本的视野投影完成。合成虚拟样本如果与拟议的数据平衡战略相结合,可以极大地提高反假面工作绩效。我们有希望的结果为利用廉价和大规模合成数据推进面部反假照片开辟了新的可能性。