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Infrared Face Recognition Dataset

This face dataset includes 125,552 RGB and infrared images of 4,484 people with 28 images per person. It covers diverse ages, genders, and ethnicities in both indoor and outdoor scenes, with full metadata. Designed for face recognition, detection, and biometric verification, it supports computer vision and infrared recognition research.

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  • people
    4,484
  • images
    125, 500+
  • Facial Recognition
  • Liveness Detection
  • Security
  • Computer Vision
  • Machine learning

This face dataset includes 125,552 RGB and infrared images of 4,484 people with 28 images per person. It covers diverse ages, genders, and ethnicities in both indoor and outdoor scenes, with full metadata. Designed for face recognition, detection, and biometric verification, it supports computer vision and infrared recognition research.

Get in touch Download sample
  • Facial Recognition
  • Liveness Detection
  • Security
  • Computer Vision
  • Machine learning
  • people
    4,484
  • images
    125, 500+

Dataset Info

Characteristic Data
Description Images of people for infrared face recognition
Data types Image
Tasks Face recognition, Computer Vision
Total number of files 125 552
Number of people 4,484
Number of files in a set 28 images for each person (RGB + IR)
Labeling Metadata (ID, nationality, gender, age, facial action, collecting scene)
Gender Male, Female
Ethnicity Asian, Latin American, Caucasian, African
Collecting scene Indoor, outdoor
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
Image Resolution 1920х1080
Camera Information File Format txt
Accuracy of Label Annotation not less than 97%
Device DV-DH4,044S305AD
Source and collection methodology. Data was collected by a partner of Unidata.

Dataset Use Cases

  • Security & Surveillance

    Improving Infrared Face Recognition in Low-Light Environments

    Infrared Face Recognition Dataset helps develop recognition algorithms that work in darkness or poor lighting conditions. With thermal infrared imagery, researchers can improve face detection and identity verification using surveillance cameras. This dataset enhances recognition systems by training models on thermal patterns and human faces captured under varied conditions.

  • Biometrics & Authentication

    Enhancing Identity Verification with Thermal Imaging

    This dataset supports face verification and recognition tasks in biometric systems. By providing infrared thermal images, developers can build solutions that resist spoofing attempts, such as photo or video attacks. The dataset improves recognition accuracy for face recognition, making it ideal for secure authentication systems and access control.

  • Healthcare & Research

    Studying Facial Features with Infrared Cameras

    Medical researchers can use Infrared Face Recognition Dataset to analyze facial features, expressions, and thermal patterns linked to health conditions. The database contains high-quality infrared imagery useful for exploring blood flow, stress levels, or fatigue detection. Such datasets, consisting of infrared spectra, support innovations in computer vision for healthcare diagnostics.

  • AI & Computer Vision

    Training Deep Learning Models for Robust Recognition

    The face recognition dataset provides reliable training data for deep learning and detection algorithms. With diverse samples of facial expressions, different poses, and infrared spectrums, the databases consist of valuable material for object detection and video surveillance projects. It helps improve detection performance and recognition technology in complex environments.

FAQs

What should I consider before buying Infrared Face Recognition Dataset?
When purchasing the dataset, review the image resolution, number of participants, and annotation details. Ensure it meets your project goals in facial recognition, recognition algorithms, or identity verification under different lighting conditions and environments.
What types of annotations are provided?
Annotations include detailed metadata such as ID, gender, age, nationality, and facial actions. This labeling helps train recognition systems to identify facial landmarks, detect different poses, and improve detection performance.
How was the data collected?
The dataset was recorded with DV-DH4,044S305AD devices using both infrared thermal cameras and RGB digital cameras. Images were captured in indoor and outdoor environments, ensuring varied lighting conditions and accurate infrared imagery.
In which formats is the dataset provided?
The dataset is delivered in JPG image format with accompanying TXT camera information files. These formats are compatible with computer vision pipelines, deep learning frameworks, and recognition technology systems.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All Unidata datasets fully comply with GDPR and other international privacy laws. Infrared Face Recognition Dataset was collected from legally authorized sources to ensure the ethical collection of data, secure processing, and responsible use in biometric recognition and computer vision applications.
How are Unidata datasets stored?
Unidata securely stores all datasets, including Infrared Face Recognition Dataset, on AWS cloud infrastructure. Storage and management practices comply with ISO 27001 and ISO 27701 standards, ensuring a privacy-first environment that supports large-scale face datasets used in deep learning and facial detection research.
How long does it take to receive the dataset?
Once a request is submitted, the Unidata team verifies the details, completes documentation, and issues an agreement for signing. After payment is processed, Infrared Face Recognition Dataset will be delivered within 3–10 days, ready for use in facial recognition training, infrared image analysis, and AI-driven biometric systems.
Is it unique data?
Yes. This dataset contains 125,552 high-resolution RGB and infrared images of 4,484 individuals captured under varying lighting conditions and facial expressions. Each image is accurately labeled with age, gender, and scene metadata, making it a unique and diverse biometric dataset for infrared-based recognition research.
Still have questions about using Unidata datasets? Read our user-guides

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