Multi-Frames Temporal Abnormal Clues Learning Method for Face Anti-Spoofing

Abstract

Face anti-spoofing researches are widely used in face recognition and has received more attention from industry and academics. In this paper, we propose the EulerNet, a new temporal feature fusion network in which the differential filter and residual pyramid are used to extract and amplify abnormal clues from continuous frames, respectively. A lightweight sample labeling method based on face landmarks is designed to label large-scale samples at a lower cost and has better results than other methods such as 3D camera. Finally, we collect 30,000 live and spoofing samples using various mobile ends to create a dataset that replicates various forms of attacks in a real-world setting. Extensive experiments on public OULU-NPU show that our algorithm is superior to the state of art and our solution has already been deployed in real-world systems servicing millions of users.

arXiv comment6 pages,7 figures,The 34th International Conference on Software Engineering & Knowledge Engineering

References in corpus (9)

• NAS-FAS: Static-Dynamic Central Difference Network Search for Face Anti-Spoofing
• Exploiting temporal and depth information for multi-frame face anti-spoofing
• Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision
• Searching Central Difference Convolutional Networks for Face Anti-Spoofing
• Aurora Guard: Real-Time Face Anti-Spoofing via Light Reflection
• Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
• Face Anti-Spoofing Via Disentangled Representation Learning
• TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection
• Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofing