SOTAVerified

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.

( Image credit: Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing )

Title	Date	Tasks	Status	Hype
Deep Residual Learning for Image Recognition	Dec 10, 2015	Classification	CodeCode Available	4
face anti-spoofing based on color texture analysis	Nov 19, 2015	Face Anti-SpoofingTexture Classification	CodeCode Available	0
Very Deep Convolutional Networks for Large-Scale Image Recognition	Sep 4, 2014	Activity Recognition In VideosClassification	CodeCode Available	1
Learn Convolutional Neural Network for Face Anti-Spoofing	Aug 24, 2014	Face Anti-Spoofing	CodeCode Available	1

Face Anti-Spoofing

Papers