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USE CASE

Low Quality Video Collection

Live-recorded attacks captured via low-quality web-cameras. The dataset solves the tasks of liveness detection and security
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Anti-Spoofing Techniques used to prevent fraudulent attempts to deceive an identifying system
Facial Recognition Process of identifying or verify a person identity using facial features
Liveness Detection The ability to distinguish whether biometric data is being captured from a live or fake source
Computer Vision Ability of a machine to interpret, analyze, and understand visual data
1 000
video
8 weeks
project duration

Our Partners

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TECHNICAL SPECIFICATIONS

Live videos of people collected from crowdsourcing platforms

Different video resolutions: QVGA (320 x 240p), QQVGA (160 x 120 p), QCIF (176 x 144 p)

Types of light: indoor daytime and evening lighting

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APPLICATION AREAS OF THE DATASET

Biometric authentication:

Data oin the dataset for developing biometric authentication systems for secure access to devices and applications

Anti-spoofing:

Data in the dataset to train the system to detect a real person in a video

Computer Vision:

The dataset can be used to detect and track facial landmarks, such as the position of the eyes, nose, and mouth

Virtual Reality and Gaming:

Data in the dataset to enhance the immersion and realism of virtual reality experiences
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DIDN'T FIND THE NECESSARY INFORMATION?

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Why
Training Data

  • Quality Assurance:
  • Enhanced Data Accuracy
  • Consistency in Labels
  • Reliable Ground Truth
  • Mitigation of Annotation Biases
  • Cost and Time Efficiency
  • Data Security and Confidentiality:
  • GDPR Compliance
  • Non-disclosure agreement
  • Data Encryption
  • Multiple data storage options
  • Access Controls and Authentication
  • Expert Team:
  • 6 years in industry
  • 35 top project managers
  • 40+ languages
  • 100+ countries
  • 250k+ assessors
  • Flexible and Scalable Solutions:
  • 24/7 availability of customer service
  • 100% post payment
  • $550 minimum check
  • Variable Workload
  • Customized Solutions

Team leads project

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Alexey Antushenya
Operations manager
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Maria Kuzmina
Project manager
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Sergey Razumny
TeamLead Crowd Solutions
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