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Synthetic Passports Dataset

The passport dataset comprises synthetic document images from multiple countries with metadata and is designed for training AI models in face recognition, identity verification, and document analysis to detect fake passports and prevent identity fraud

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Image with background
  • Images
    100 000
  • Countries
    100+
  • Know your customer
  • Data generation
  • Computer Vision
  • Security
  • Anti-spoofing
  • Images
    100 000
  • Countries
    100+

Dataset Info

Characteristic Data
Description Generated passports for training a neural network to identify a document
Data types Image
Tasks Face recognition, Computer Vision
Total number of files 100 000
Number of countries 100+
Labeling Metadata (background)
Gender Male, Female
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Technical
Characteristics

Characteristic Data
Image Extensions png
Data Type generated
Source and collection methodology. Data was AI-generated.

Dataset Use Cases

  • Financial Services

    Training Identity Verification Systems

    Banks and fintech companies use Synthetic Passport Dataset to strengthen fraud detection and onboarding workflows. Since the dataset includes passport images from different countries, models learn to identify ID documents accurately without exposing personal data. This enables safer, compliant, and scalable identity verification solutions built on synthetic generation technology.



  • Border Control & Security

    Document Recognition for Immigration Systems

    Security agencies apply passport datasets to improve automated checks at airports and border crossings. The dataset contains images generated to represent multiple document types, ensuring systems recognize variations in ID cards and identity documents. By using synthetic data, authorities train verification systems without relying on sensitive personal information.



  • Technology & AI Development

    Building Machine Learning Models

    This dataset provides a rich training base for learning models focused on document analysis and text recognition. Since the dataset consists of thousands of high-quality synthetic ID images, researchers can experiment with different types of layouts, fonts, and structures. This supports advancements in computer vision and intelligent recognition systems.



  • Education & Research

    Training Without Sensitive Data Exposure

    Universities and research labs rely on the synthetic passport dataset as an alternative to public datasets containing real identity documents. Because the images generated are free from personal data, students and professionals can explore document analysis methods safely. This promotes innovation in synthetic ID research while maintaining compliance with privacy standards.



What is Synthetic Passports Dataset used for?
It is designed for training learning models in computer vision, fraud detection, and identity verification. It helps develop systems for detecting fake documents, preventing identity fraud, and improving verification systems that process passport photos and other identification documents.
What is included in this dataset?
This passport dataset contains 100,000 images generated to simulate identity documents from more than 100 countries. The dataset includes passport photos, synthetic identity details, and metadata such as background information.
Is it possible to request a custom dataset?
Yes, Unidata provides the option to create custom datasets tailored to your project needs. You can specify the data sources, annotation types, or formats, and the dataset will be collected and labeled according to your requirements.
What should I consider before buying this dataset?
When purchasing it, consider the document types, document layout, and country coverage to ensure it meets your project’s needs. Since the dataset consists of synthetic datasets rather than real identity documents, it is best suited for training anti-spoofing, verification solutions, and computer vision models without handling actual personal information.
Still have questions about using Unidata datasets? Read our user-guides

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