Live-videos of people of different genders, ages and ethnicities, shot on high-resolution webcams. The dataset effectively solves the tasks of liveness detection and security

Full HD Webcam Live Attacks

Ability of a machine to interpret, analyze, and understand visual data
Computer Vision
Process of identifying or verify a person identity using facial features
Facial Recognition
The ability to distinguish whether biometric data is being captured from a live or fake source
Liveness Detection
Techniques used to prevent fraudulent attempts to deceive an identifying system
3 000
video files
4 weeks
project duration
Technical specifications
Live videos of people collected from crowdsourcing platforms
Types of light: indoor daytime and evening lighting
Different video resolutions: Full HD (1080p), QHD (1440p), 4K (2160p)
Unique attack identifier
Identifier of the user recording the attack
User's age
User's gender
Metadata is represented in the file_info.csv.
Each attack instance is accompanied by the following details:
User's country of origin
Attack resolution
Model of the webcam
Extra Services
Data collection of Live selfies and videos of people from webcams with a resolution from Full HD to 4K with a volume and metadata on demand
Data Labelling: Bounding Box and classification for selfies and videos
Collection of photos with street and home lighting at different times of the day
Collection of real-video and attacks from devices of interest (attacks from MacBook, iPad and iPhone webcams)
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