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ID Re-identification Dataset

It is a large-scale synthetic ID dataset comprising over 200,000 annotated images, including ID photos and real-life selfies of nearly 30,000 individuals, designed for training facial recognition, ID matching, and person re-identification systems in computer vision and surveillance applications, with diverse camera viewpoints, demographics, and metadata.

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  • images
    200,000+
  • people
    29,500+
ID Re-identification Dataset
  • Facial Recognition
  • Computer Vision
  • Machine learning
  • Anti-spoofing
  • Security

It is a large-scale synthetic ID dataset comprising over 200,000 annotated images, including ID photos and real-life selfies of nearly 30,000 individuals, designed for training facial recognition, ID matching, and person re-identification systems in computer vision and surveillance applications, with diverse camera viewpoints, demographics, and metadata.

Get in touch Download sample
  • Facial Recognition
  • Computer Vision
  • Machine learning
  • Anti-spoofing
  • Security
  • images
    200,000+
  • people
    29,500+

Dataset Info

Characteristic Data
Description Selfie and ID image of people for facial recognition
Data types Image
Tasks Facial recognition, Computer Vision
Number of images 200,000+
Number of files in a set ID photo and 5-10 life photos per person
Number of people 29,523
Labeling Metadata (ID, age, gender, ethnicity)
Gender Male, Female
Ethnicity Asian, Caucasian, African, Mexicans
Collecting scene Indoor, outdoor
Age Young people, middle-aged, elderly
ID Re-identification Dataset
ID Re-identification Dataset
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Statistics

Distribution by ethnicity
Distribution by gender

Technical
Characteristics

Characteristic Data
Image extension Jpg, jpeg, png
Accuracy of label annotation more than 97%
Source and collection methodology. Data was collected by a partner of Unidata.

Dataset Use Cases

  • Security & Surveillance

    Improving Person Re-Identification Across Multiple Cameras

    ID Re-identification Dataset is used to train re-identification systems that detect the same individuals across various camera viewpoints in video surveillance environments. Containing annotated identity data and high-quality synthetic photos, it supports deep learning models used for pattern recognition, tracking, and security monitoring in real-world applications.

  • Identity Verification Systems

    Enhancing Accuracy in ID Matching and Validation

    This identity verification dataset helps develop and validate ID matching systems used in banking, border control, and access management. The dataset consists of diverse synthetic ID images created to reflect different lighting and angles, allowing computer vision models to recognize the same identity across multiple re-ID tasks with improved reliability.

  • Research and Model Benchmarking

    Training Deep Neural Networks for Re-ID Tasks

    Researchers rely on this ID re-identification dataset to benchmark and improve deep neural networks for re-identification tasks. The dataset comprises thousands of synthetic images of people and objects, providing training data for learning algorithms to evaluate recognition performance and enhance re-identification accuracy.

  • Smart Retail and Crowd Analytics

    Identifying Repeated Appearances in Public Spaces

    This dataset also supports re-identification systems used in smart retail analytics and crowd behavior studies. Simulating the same individuals appearing in different camera views helps build object detection and re-ID models that can track customer movement patterns, detect re-identification risks, and improve in-store monitoring systems using AI-based visual analysis.

FAQs

What is included in this dataset?
The dataset includes ID photos paired with 5–10 life images per individual, totaling over 200,000 images. Each sample is annotated with metadata such as age, gender, and ethnicity, providing a robust foundation for re-identification systems and AI model training.
Can I request a sample of the dataset before purchasing or downloading it?
Yes. Unidata provides free dataset samples that allow you to evaluate image quality, labeling accuracy, and metadata structure before purchase. This helps ensure the dataset meets your AI model training or identity verification requirements.
What are the sources of data for this dataset?
All images in the ID Re-identification Dataset were collected by a verified Unidata partner through controlled and ethical data sourcing processes. Data was captured using standardized imaging setups in indoor and outdoor environments to ensure diversity and realism.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model: free samples are offered for testing and evaluation, while full datasets are available through purchase. This ensures transparency, allowing users to verify dataset quality before committing to a full acquisition.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All datasets, including the ID Re-identification Dataset, comply with GDPR and international data protection regulations. Unidata collects data only from legally permissible and ethical sources, ensuring the protection of personal and biometric information.
How are Unidata datasets stored?
Unidata securely stores all datasets on AWS cloud infrastructure, ensuring high availability, data integrity, and scalability. Storage systems are managed in compliance with ISO 27001 and ISO 27701 standards, guaranteeing a secure and privacy-compliant environment for sensitive data.
How long does it take to receive the dataset?
Once your request is submitted, Unidata will contact you to confirm details and complete documentation. After signing and payment, the dataset is typically delivered within 3–10 business days via a secure download link.
Is it unique data?
Yes. The ID Re-identification Dataset is proprietary and exclusive to Unidata. Its combination of ID and selfie pairs across thousands of individuals makes it a unique large-scale dataset.
Still have questions about using Unidata datasets? Read our user-guides

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