A2A | Anti-Spoofing | Machine Learning (ML) | Multimodal Video Learning Data | Deep Fake

A2A's EyeD Multi-modal image learning to prevent forgery and tampering in the field of face recognition | Video data for liveness detection | As the use of facial recognition solutions expands, it is difficult to pass identity authentication by forging the face in various ways. Deep learning is used to detect face spoofing attempts (Face Anti-spoofing or Face Liveness Detection). AI face recognition algorithms must undergo sufficient learning based on various data, but currently there is not enough dataset for learning facial forgery detection. Therefore, after photographing the subject with RGB-Depth-IR, RGB-Depth, and RGB-IR cameras,Face forgery by securing a large amount of face forgery attack data using prints, video replays, and 3D masks. Provided: Multimodal Video Learning Data Sets RGB + Depth + IR