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Custom Face Recognition Image Dataset

It is a large-scale face recognition dataset comprising 500,600 well-annotated face images with metadata (ID, age, gender, ethnicity), collected in varied indoor and outdoor scenes, built to support facial recognition, emotion classification, landmark detection, and deep learning–based computer vision research.

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  • images
    500,600+
  • Facial Recognition
  • Computer Vision
  • Machine learning
  • Anti-spoofing

It is a large-scale face recognition dataset comprising 500,600 well-annotated face images with metadata (ID, age, gender, ethnicity), collected in varied indoor and outdoor scenes, built to support facial recognition, emotion classification, landmark detection, and deep learning–based computer vision research.

Get in touch Download sample
  • Facial Recognition
  • Computer Vision
  • Machine learning
  • Anti-spoofing
  • images
    500,600+

Dataset Info

Characteristic Data
Description Image of people for facial recognition
Data types Image
Tasks Facial recognition, Computer Vision
Number of images 500,600
Labeling Metadata (ID, age, gender, ethnicity)
Gender Male, Female
Ethnicity Asian, Caucasian, African, Middle Eastern
Collecting scene Indoor, outdoor
Age Young people, middle-aged
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Statistics

Distribution by gender
Distribution by ethnicity

Technical
Characteristics

Characteristic Data
Video extension jpg
Accuracy of label annotation not less than 95%
Source and collection methodology. Data was collected by a partner of Unidata.

Dataset Use Cases

  • Security and Access Control

    Enhancing Identity Verification Systems

    Custom Face Recognition Image Dataset supports the development of high-accuracy recognition systems used in access control, surveillance, and authentication. With diverse human faces, lighting conditions, and facial expressions, it helps train models to recognize individuals reliably across environments – improving security and reducing false positives in real-world detection systems.

  • Retail and Customer Analytics

    Understanding Shopper Behavior Through Facial Recognition

    Retailers use this face image dataset to train recognition algorithms that detect customer demographics, emotions, and engagement. By analyzing facial features and expressions, AI systems can identify returning customers, study shopping patterns, and personalize marketing experiences – bridging data-driven insights with improved in-store service and customer satisfaction.

  • Healthcare and Patient Monitoring

    Facial Analysis for Emotional and Physical Well-Being

    This dataset provides training data for models that detect emotions, fatigue, or pain through subtle facial cues. Hospitals and telemedicine platforms use facial analysis and recognition techniques to monitor patient states remotely, ensuring better care and early detection of emotional or health-related changes during consultations.

  • AI Research and Development

    Building Advanced Face Recognition Algorithms

    Researchers rely on this face recognition dataset for algorithm benchmarking and facial recognition research. It supports deep learning experiments in facial keypoint detection, face verification, and anti-spoofing. The comprehensive dataset comprising real and synthetic faces allows testing of recognition techniques under controlled and natural conditions, advancing computer vision innovation.

FAQs

What is Custom Face Recognition Image Dataset used for?
This dataset is designed for training and evaluating face recognition and detection algorithms. It supports a wide range of applications, including facial identification, face verification, emotion recognition, and other computer vision tasks that require accurate facial feature analysis.
What is included in this dataset?
The dataset consists of over 500,000 high-resolution images representing diverse individuals across different ethnicities, genders, and age groups. Each image is labeled with detailed metadata, including ID, age, gender, and ethnicity, ensuring structured and comprehensive training data for recognition algorithms.
Can I request a sample of the dataset before purchasing or downloading it?
Yes. Unidata datasets follow a dual-licensing model, offering free samples for trial and testing. These samples allow you to assess image quality, labeling precision, and overall suitability for your facial recognition or computer vision project before making a purchase.
How was the data collected?
All images were collected by a verified Unidata partner under controlled indoor and outdoor environments. This approach ensures consistent lighting conditions, diverse backgrounds, and realistic human facial variations for high-quality facial recognition training sets.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model: free samples are available for trial and testing, while full datasets are accessible exclusively through purchase. This licensing structure supports transparent evaluation and ensures responsible data usage.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All Unidata datasets fully comply with GDPR and other data protection regulations. Each dataset is curated using legally obtained images to guarantee ethical data collection and privacy protection for subjects represented in the dataset.
How are Unidata datasets stored?
Unidata securely stores all datasets on AWS cloud infrastructure, providing reliable access and strong data protection. Our storage complies with ISO 27001 and ISO 27701 standards, ensuring that facial image files, annotations, and metadata remain safe and privacy-focused.
How long does it take to receive the dataset?
After you submit a request, Unidata’s team reviews the details and prepares the documentation. Once the agreement is signed and payment is completed, dataset delivery typically takes between 3 and 10 days.
Is this a real-world dataset or synthetic data?
This dataset contains real-world facial images captured from actual individuals in varied environments. Unlike synthetic datasets or AI-generated faces, this collection provides authentic visual diversity for developing and testing accurate recognition systems and computer vision applications.
Still have questions about using Unidata datasets? Read our user-guides

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