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Anti-Spoofing Real Videos Dataset

The dataset comprises anti-spoofing real videos with paired images of human faces, featuring diverse resolutions, demographics, and metadata to support face anti-spoofing research, spoofing detection, liveness verification, and the development of biometric authentication systems against presentation attacks

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  • Files
    87,340
  • Countries
    179
  • People
    43,670
  • Facial Recognition
  • iBeta
  • Liveness Detection
  • Security
  • Anti-spoofing

The dataset comprises anti-spoofing real videos with paired images of human faces, featuring diverse resolutions, demographics, and metadata to support face anti-spoofing research, spoofing detection, liveness verification, and the development of biometric authentication systems against presentation attacks

Get in touch Download sample
  • Facial Recognition
  • iBeta
  • Liveness Detection
  • Security
  • Anti-spoofing
  • Files
    87,340
  • Countries
    179
  • People
    43,670

Dataset Info

Characteristic Data
Description Live videos of people for anti-spoofing tasks
Data types Image, Video
Tasks Face recognition, face detection
Number of people 43,670
Number of files in a set 2 (1 image & 1 video)
Total number of video 87,340
Labeling Only technical characteristics and metadata (age, gender, ethnicity)
Gender Male, Female
Ethnicity Asian (30%), African (70%)
Age Min = 18, max = 80, mean = 45
Number of countries 179
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Statistics

Gender distribution (Number)
Gender distribution (%)
Ethnicity distribution (COUNTA of ethnicity)
Ethnicity distribution (%)
Resolution distribution (COUNTA of resolution)
Resolution distribution (%)
Continent distribution

Technical
Characteristics

Characteristic Data
Video Extensions mp4, MOV
Image Extension jpg
Video Resolutions 1920 x 1080p, 480 x 360p, 1280 x 720p, 720 x 480p, 640 x 480p, 1920 x 920p
Video Duration Mean = 9, median = 9, min = 2, max = 34
Frames per second Mean = 26.6
Devices iPhone 13 (30%), Google Pixel (70%)
Data source. Data was collected via crowdsourcing platforms.

Dataset Use Cases

  • Financial Services & Digital Banking

    Preventing Facial Fraud in Biometric Authentication

    Banks and fintech platforms rely on facial recognition for secure onboarding and identity verification. This face anti-spoofing dataset provides real video footage of spoofing attacks, such as photo attacks, 3D masks, and printed photos, helping improve liveness detection and prevent biometric fraud across online authentication systems.

  • Mobile Devices & Consumer Electronics

    Enhancing Smartphone Unlock Security

    Face unlock features are often targets for spoofing attempts. With real-world samples of anti-spoofing techniques, this dataset allows manufacturers to train anti-spoofing algorithms that distinguish between real faces and fake faces. It supports better liveness detection and anti-spoofing systems for everyday biometric security on consumer devices.

  • Government & Border Control

    Securing Access with Strong Liveness Detection

    This dataset helps improve biometric authentication systems used in airports, border control, and secure facilities. By providing real examples of presentation attacks and spoofing techniques, it supports the development of accurate anti-spoofing technology that can reliably detect real persons during identity verification at checkpoints and kiosks.

  • Research & Development

    Training Robust Anti-Spoofing Models

    This dataset is ideal for training deep learning models in academic and industry research. With high-quality video samples of spoofing detection scenarios, it helps test and refine state-of-the-art methods, including convolutional networks, texture analysis, and facial movements – pushing forward the development of more accurate anti-spoofing solutions.

FAQs

What is Anti-Spoofing Real Videos Dataset used for?
The dataset is designed to develop and evaluate anti-spoofing algorithms, detection techniques, and biometric authentication systems. It supports face recognition, liveness detection, and the prevention of spoofing attacks such as photo attacks, 3D masks, and print attacks in modern biometric security systems.
What is included in this dataset?
It provides videos of 43,670 people with one image and one video, for a total of 87,340 files. It includes anti-spoofing real videos, RGB images, and metadata such as age, gender, and ethnicity, making it suitable for training deep learning and anti-spoofing methods.
What types of annotations are provided?
The dataset is labeled with technical characteristics and metadata, including participant age, gender, and ethnicity. These annotations help create structured training datasets for spoofing detection and facial recognition research.
Is this dataset real or synthetic?
The face anti-spoofing dataset consists of real videos and images of genuine participants, not synthetic or artificially generated. This ensures reliable training data for testing anti-spoofing technology against known attacks and enhancing biometric security.
How long does it take to receive the dataset?
Once you submit a request, we will reach out to review the details and complete the necessary documents. After signing and payment, the dataset will be delivered within 3-10 days, allowing you to begin biometrics testing, face recognition, liveness detection, and attack detection in biometric systems promptly.
How are Unidata datasets licensed?
Unidata datasets follow a dual licensing model: evaluation samples are provided for free, while the full dataset is accessible only by purchase.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. Each dataset follows GDPR guidelines and complies with applicable data protection laws. Data is carefully collected from legal sources to ensure responsible usage.
How are Unidata datasets stored?
Unidata secures all datasets within AWS’s cloud platform, built for flexibility and continuous availability. We adhere to ISO 27001 and ISO 27701 certifications, which uphold the highest standards in information security and privacy management. This commitment provides our clients with confidence in data safety and reliability.
Still have questions about using Unidata datasets? Read our user-guides

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