Two-stream Convolutional Networks for Multi-frame Face Anti-spoofing
arXiv:2108.04032
Abstract
Face anti-spoofing is an important task to protect the security of face recognition. Most of previous work either struggle to capture discriminative and generalizable feature or rely on auxiliary information which is unavailable for most of industrial product. Inspired by the video classification work, we propose an efficient two-stream model to capture the key differences between live and spoof faces, which takes multi-frames and RGB difference as input respectively. Feature pyramid modules with two opposite fusion directions and pyramid pooling modules are applied to enhance feature representation. We evaluate the proposed method on the datasets of Siw, Oulu-NPU, CASIA-MFSD and Replay-Attack. The results show that our model achieves the state-of-the-art results on most of datasets' protocol with much less parameter size.
References in corpus (9)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
- Feature Pyramid Networks for Object Detection
- Learn Convolutional Neural Network for Face Anti-Spoofing
- Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
- Exploiting temporal and depth information for multi-frame face anti-spoofing
- Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision
- Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
- Enhance the Motion Cues for Face Anti-Spoofing using CNN-LSTM Architecture