Two-stream Convolutional Networks for Multi-frame Face Anti-spoofing
This addresses security vulnerabilities in face recognition systems for applications like authentication, though it is incremental as it builds on existing video classification methods.
The paper tackles face anti-spoofing by proposing a two-stream convolutional network that uses multi-frames and RGB difference inputs to capture differences between live and spoof faces, achieving state-of-the-art results on most datasets with significantly fewer parameters.
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.