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iBeta Level 2 Dataset

The Dataset for successfully obtaining the iBeta Lab certificate in "Presentation Attack Detection" Level 2

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  • Videos
    32,300
  • iBeta Level 2 Completion
    100%
  • Types of Attacks
    6
  • Facial Recognition
  • iBeta
  • Liveness Detection
  • Security
  • Anti-spoofing
  • Computer Vision
  • Videos
    32,300
  • iBeta Level 2 Completion
    100%
  • Types of Attacks
    6

Dataset Info

Characteristic Data
Description Videos of people for training algorithms to detect attempts to hack biometric systems.
Data types Video
Tasks Face recognition, Computer Vision
Total number of files 32 300
Labeling Only technical characteristics and metadata (age, gender, ethnicity)
Gender Male, Female
Ethnicity Caucasian (90%), African (10%)
Number of attributes 31
Type of attack Real Person, 2D mask, 2D mask with eyeholes, latex mask, wrapped 3D mask, silicone mask
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iBeta Level 2 Dataset

Statistics

Attacks in the dataset
Gender of the unique people:
Ethnicity of the unique people:

Technical
Characteristics

Characteristic Data
Video Extensions mp4, MOV
Video Resolutions Min = 1920х1080, Max = 3840х2160
Video duration 4 second
Number of backgrounds 9
Video overlap No more than 5%
Devices Mi10s, Google Pixel 4, Samsung Galaxy A03s, iPhone 11, iPhone SE 2, etc.
Source and collection methodology: Data was collected via crowdsourcing platforms.

Industries

Finance

  • Banking:

    Using biometric authentication for secure access to bank accounts and transactions.

  • E-commerce:

    Using facial recognition technologies to prevent fraud in online purchases and authenticate users.

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Security

  • Access Control Integration:

    Implementation of solutions based on iBeta Level 2 to enhance security levels across various sectors, including physical security and access to digital resources.

  • Biometric Verification:

    A method of confirming identity using unique biological traits, such as facial features, to authenticate individuals

  • Cybersecurity:

    Applying biometric attack detection techniques for fraud defense

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