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Liveness Detection Video Dataset

It is a large-scale lieveness detection dataset comprising over 248,000 videos and 66,000 images of diverse individuals, designed for training liveness detection algorithms, facial recognition systems, and anti-spoofing technologies to identify real faces and prevent spoofing attacks, including replay attacks, under varied lighting conditions and environments.

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  • videos
    248,000+
  • images
    66,500+
  • people
    1,000+
  • Facial Recognition
  • Computer Vision
  • Machine learning
  • Anti-spoofing
  • Liveness Detection
  • Security

It is a large-scale lieveness detection dataset comprising over 248,000 videos and 66,000 images of diverse individuals, designed for training liveness detection algorithms, facial recognition systems, and anti-spoofing technologies to identify real faces and prevent spoofing attacks, including replay attacks, under varied lighting conditions and environments.

Get in touch Download sample
  • Facial Recognition
  • Computer Vision
  • Machine learning
  • Anti-spoofing
  • Liveness Detection
  • Security
  • videos
    248,000+
  • images
    66,500+
  • people
    1,000+

Dataset Info

Characteristic Data
Description Image and video of people for liveness detection
Data types Image, video
Tasks Liveness Detection, Computer Vision
Number of images 66 528
Number of videos 248 160
Total number of people 1 056
Labeling Metadata (ID, age, gender, ethnicity)
Gender Male, Female
Ethnicity Asian, Caucasian, African
Collecting scene Indoor(776), outdoor(280)
Age Young people, middle-aged, elderly
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Statistics

Distribution by gender
Distribution by ethnicity
Distribution by age

Technical
Characteristics

Characteristic Data
Image extension jpg
Video extension mov. mp4
Accuracy of actions not less than 97%
Accuracy of naming the actions and lip language not less than 97%
Source and collection methodology. Data was collected by a partner of Unidata.

Dataset Use Cases

  • Biometric Security and Identity Verification

    Enhancing Face Liveness Detection in Authentication Systems

    Liveness Detection Video Dataset supports the development of biometric systems that distinguish real faces from spoofing attacks such as masks, replay attacks, or deepfakes. The dataset contains thousands of video recordings of genuine and fake presentations, helping recognition algorithms verify users and ensure accurate authentication in identity verification processes.

  • Financial Services and Fraud Prevention

    Training Models to Detect Presentation and Replay Attacks

    This presentation attack dataset is widely used in financial applications to prevent identity fraud and unauthorized access. Including spoofing attempts captured under different lighting conditions and camera angles, it allows deep learning models to detect fake faces in video streams, significantly reducing fraud attempts in mobile banking and online payment systems.

  • Research and Development in AI Security

    Benchmarking Deep Neural Networks for Anti-Spoofing Tasks

    The dataset is a valuable resource for researchers building anti-spoofing solutions using deep neural networks. The dataset comprises real and synthetic human faces performing specific actions, enabling the evaluation of detection algorithms in face presentation and object detection tasks, and advancing research in AI-driven biometric authentication.

  • Access Control and Surveillance Systems

    Improving Real-Time Liveness Recognition in Security Devices

    This dataset provides training data for surveillance systems and smart access controls that require liveness detection in real-time. Simulating attack scenarios such as spoofing techniques and presentation attacks strengthens recognition systems used in public safety, corporate security, and entry verification, ensuring only living persons are granted access.

FAQs

What types of annotations are provided?
Annotations include metadata for each individual, detailing identity, age, gender, ethnicity, and scene type (indoor/outdoor). Labels for actions and lip movements are accurate to 97% or higher, supporting reliable liveness detection and facial analysis.
Can I request a sample of the dataset before purchasing or downloading it?
Yes. Unidata offers free samples so you can evaluate image quality, video resolution, and labeling structure before committing. This ensures the dataset meets your liveness detection or spoofing prevention requirements.
Is it possible to request a custom dataset?
Yes. You can request a custom dataset tailored to specific attack types, lighting conditions, or age groups. Unidata can provide datasets optimized for biometric systems, deepfake detection, and facial recognition training.
What are the sources of data for Unidata datasets?
All images and videos in this dataset were collected by a verified partner of Unidata using ethical and legally compliant methods. Data was captured in indoor and outdoor scenes to ensure a diverse and realistic set of facial appearances and attack scenarios.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model: free samples are provided for evaluation, while the full dataset is available exclusively for purchase. This allows users to verify dataset quality and relevance before acquiring the complete set.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. The dataset complies with GDPR and international data protection laws. Data is collected only from ethically sourced and legally permissible participants, ensuring privacy and lawful usage in research and commercial applications.
How are Unidata datasets stored?
Unidata stores all datasets on AWS cloud infrastructure, providing high availability, security, and scalability. Storage and management comply with ISO 27001 and ISO 27701 standards, ensuring a privacy-focused environment for sensitive biometric data.
How long does it take to receive the dataset?
After submitting a request, Unidata will contact you to review details and complete documentation. Once signed and paid, the dataset is delivered securely within 3–10 business days.
Still have questions about using Unidata datasets? Read our user-guides

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