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Face Re-identification Image Dataset

It is a large-scale face identification dataset containing over 670,000 annotated images of 23,000+ individuals captured in varied angles, lighting, and environments, designed for training face recognition, face detection, and person re-identification systems in surveillance applications, with rich metadata and diverse facial expressions, poses, and backgrounds.

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  • people
    23,110
  • images
    670,000+
  • Facial Recognition
  • Computer Vision
  • Machine learning
  • Security

It is a large-scale face identification dataset containing over 670,000 annotated images of 23,000+ individuals captured in varied angles, lighting, and environments, designed for training face recognition, face detection, and person re-identification systems in surveillance applications, with rich metadata and diverse facial expressions, poses, and backgrounds.

Get in touch Download sample
  • Facial Recognition
  • Computer Vision
  • Machine learning
  • Security
  • people
    23,110
  • images
    670,000+

Dataset Info

Characteristic Data
Description Images of people featuring various angles, backgrounds, and attributes for facial expression recognition
Data types Image
Tasks Face Re-identification, Computer Vision
Total number of image 670 190
Number of people 23 110
Number of files in a set 28 images and 1 ID photo for each person
Labeling Metadata (ID, nationality, gender, age, emotion, collecting scene)
Gender Male, Female
Ethnicity Asian, Mexican, Caucasian, African, Indian
Collecting scene Indoor and outdoor scenes
Age Teenagers, young adults, middle-aged, elderly
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Statistics

Distribution by gender
Distribution by ethnicity

Technical
Characteristics

Characteristic Data
Image extension Jpg, png
Accuracy of labels of face pose is more than 97%
Device Phone
Source and collection methodology. Data was collected by a partner of Unidata.

Dataset Use Cases

  • Security & Surveillance

    Enhancing Person Re-Identification in Video Surveillance Systems

    Ths dataset supports surveillance applications by providing high-quality face images and video sequences captured under various conditions. It enables recognition systems to accurately match unique identities across different cameras, improving facial recognition accuracy in public safety and security monitoring solutions.

  • AI Research & Model Benchmarking

    Evaluating Deep Learning Models for Face Recognition

    Researchers use this face re-identification dataset to benchmark recognition technology and test deep neural networks in facial recognition tasks. The dataset consists of thousands of annotated face photographs from different people, providing training data ideal for improving re-identification systems and studying face detection performance.

  • Anti-Spoofing and Fraud Prevention

    Detecting Fake or Manipulated Faces in Digital Systems

    This dataset also supports the development of face anti-spoofing models, crucial for detecting fake faces or identity fraud attempts. By including real and synthetic variations of facial images, it enables recognition algorithms to distinguish between authentic users and spoofed identities, strengthening security systems and biometric verification tools in financial and governmental sectors.

  • Identity Verification & Access Control

    Training Models for Face Detection and Verification

    This face identification dataset helps develop face verification systems used in authentication platforms, smart access control, and digital ID validation. Containing human faces with varied poses, expressions, and lighting conditions, it enhances recognition algorithms.

FAQs

What is included in this dataset?
The dataset includes 670,190 high-resolution facial images representing 23,110 individuals. Each subject has 28 facial photos and one ID-style image, labeled with metadata such as gender, age, nationality, emotion, and capture scene.
Is this a real-world dataset or synthetic data?
This is a real-world dataset, consisting of authentic images of individuals captured in everyday environments. The dataset provides natural variations in facial expressions, head poses, and lighting.
Can I request a sample of the dataset before purchasing or downloading it?
Yes. Unidata offers free dataset samples so you can assess the image quality, annotation accuracy, and format before purchase. These samples help you determine whether the dataset meets the needs of your AI research, computer vision, or facial recognition projects.
What are the sources of data for Unidata datasets?
Face Re-identification Image Dataset was collected by a partner of Unidata using smartphone cameras in diverse indoor and outdoor environments. All data sources are legally verified and adhere to ethical and privacy standards to ensure compliance with data protection laws.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model: free samples are provided for evaluation and testing, while full datasets are available exclusively through purchase. This ensures users can validate data quality before acquiring the full dataset for research or commercial use.
How are Unidata datasets stored?
All datasets are stored on AWS cloud infrastructure, offering secure, scalable, and high-availability data access. Unidata’s storage practices comply with ISO 27001 and ISO 27701 standards, ensuring the confidentiality and integrity of sensitive data such as facial images.
How long does it take to receive the dataset?
After you submit a dataset request, Unidata will contact you to review your requirements and finalize documentation. Upon contract signing and payment, delivery is completed within 3–10 business days through a secure download link.
Still have questions about using Unidata datasets? Read our user-guides

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