Facial anti-spoofing is the task of preventing false facial verification by using a photo, video, mask or a different substitute for an authorized person’s face. Some examples of attacks:
Print attack: The attacker uses someone’s photo. The image is printed or displayed on a digital device.
Replay/video attack: A more sophisticated way to trick the system, which usually requires a looped video of a victim’s face. This approach ensures behaviour and facial movements to look more ‘natural’ compared to holding someone’s photo.
3D mask attack: During this type of attack, a mask is used as the tool of choice for spoofing. It’s an even more sophisticated attack than playing a face video. In addition to natural facial movements, it enables ways to deceive some extra layers of protection such as depth sensors.
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Face Anti-spoofing gains increased attentions recently in both academic and industrial fields.
To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities.
Face anti-spoofing (a. k. a presentation attack detection) has drawn growing attention due to the high-security demand in face authentication systems.
SOTA for Face Anti-Spoofing on MSU-MFSD
Moreover, the nets trained using combined data from two datasets have less biases between two datasets.
Over the past few years, Presentation Attack Detection (PAD) has become a fundamental part of facial recognition systems.
The proposed approach achieves an HTER of 0% in Replay Mobile dataset and an ACER of 0. 42% in Protocol-1 of OULU dataset outperforming state of the art methods.
Face PAD datasets are usually captured with RGB cameras, and have very limited number of both bona-fide samples and presentation attack instruments.
We also introduce the new Wide Multi-Channel presentation Attack (WMCA) database for face PAD which contains a wide variety of 2D and 3D presentation attacks for both impersonation and obfuscation attacks.