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Face Anti-Spoofing

Facial Recognition and Modelling › Face Anti-Spoofing 31 평가 기록 8 datasets

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.

<span style="color:grey; opacity: 0.6">( Image credit: Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing )</span>

개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.

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Dataset기록된 모델 예시MetricPaperCode
FasTCo + OAP
ACER: 21.7
논문 코드
ResNet 18 Personalized
AUC: 99.2
논문 코드
ResNet18 Personalized
AUC: 99.2
논문 코드
Bi-FPNFAS
ACER: 2.92
논문 코드
Entry-V2
EER: 0HTER: 0
논문 코드
Entry-V2
Equal Error Rate: 0HTER: 0
논문 코드
3D Synthesis (balancing sampling)
EER: 2.22HTER: 1.67
논문 코드
MCCNN (BCE+OCCL)-GMM
HTER: 3.4
논문 코드

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