Acknowledgement
This research was supported by Basic Science Research Program through the NRF of Korea funded by the Ministry of Education (GR 2019R1D1A3A03103736).
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Face anti-spoofing (FAS) techniques play a significant role in the defense of facial recognition systems against spoofing attacks. Existing FAS methods achieve the great performance depending on annotated additional modalities. However, labeling these high-cost modalities need a lot of manpower, device resources and time. In this work, we proposed to use self-transforming modalities instead the annotated modalities. Three different modalities based on frequency domain and temporal domain are applied and analyzed. Intuitive visualization analysis shows the advantages of each modality. Comprehensive experiments in both the CNN-based and transformer-based architecture with various modalities combination demonstrate that self-transforming modalities improve the vanilla network a lot. The codes are available at https://github.com/chenmou0410/FAS-Challenge2021.
This research was supported by Basic Science Research Program through the NRF of Korea funded by the Ministry of Education (GR 2019R1D1A3A03103736).