Diverse Facial Image Dataset
Access a facial-image dataset built around high-resolution portraits of 5,000–10,000 unique adults aged 18–70. The collection is designed to provide broad variation across ethnicity, gender, expression, makeup, lighting, background, head pose, and occlusion conditions while maintaining strong overall image quality and clear subject visibility.
The dataset is structured for AI teams that need controlled diversity rather than generic portrait volume alone. Approximately 20% of the imagery features asymmetric expressions, while around 70% is unoccluded and captured in a mobile-shot style. All imagery is intended to meet GDPR, copyright, and explicit participant-consent requirements. That controlled expression variation makes it a common starting point for facial expression datasets.
Dataset Preview
Representative facial images showing variation in expression, ethnicity, gender, head pose, makeup, lighting, background, and occlusion conditions.
The examples illustrate the range of facial conditions available for model development, including asymmetric expressions, non-frontal poses, partial occlusion, mobile-shot imagery, and varied lighting or background context.
Key Highlights
- • The collection is designed for controlled facial variability rather than generic portrait volume alone
- • Thousands of unique adult subjects provide identity diversity without relying on a small repeated subject pool
- • Variation spans ethnicity, gender, appearance, expression, makeup, lighting, and background conditions
- • Head-pose changes provide non-frontal facial examples for pose-sensitive modelling
- • Occlusion variation supports evaluation beyond consistently unobstructed faces
- • Asymmetric-expression cases provide targeted coverage for non-neutral and uneven facial movement
- • A large unoccluded mobile-shot subset provides a practical baseline alongside harder facial conditions
- • Consistent composition and processing preserve technical quality across varied capture conditions
- • Human-authored and automatically generated stock metadata can support filtering and subset construction
- • Collection requirements explicitly address participant consent, GDPR, and copyright considerations
Metadata Fields
Example Metadata Record
title: Adult woman with slight asymmetric smile in natural daylight
description: High-resolution facial portrait of an adult woman with a slight asymmetric smile, partial side angle, visible background context, and natural daylight conditions
keywords: face, portrait, adult, woman, asymmetric expression, natural light, facial image, mobile-shot, head pose
age_range: 25–34
gender: Female
ethnicity: Available where applicable
expression_type: Asymmetric smile
asymmetry_flag: Yes
occlusion_status: Unoccluded
head_pose: Slight three-quarter angle
lighting_condition: Natural daylight
background_type: Soft residential background
makeup_visibility: Light makeup
capture_style: Mobile-shot
Technical Specifications
Dataset Type
Diverse Facial Image Dataset
Content Type
High-resolution facial imagery
Image Count
Up to 30,000 images
Unique Subjects
5,000–10,000 adults
Age Range
18–70
Visual Diversity
Ethnicity, gender, appearance, expression, makeup, background, lighting, head pose, and occlusion variation
Expression Coverage
Approximately 20% asymmetric expressions
Occlusion / Capture Coverage
Approximately 70% unoccluded and mobile-shot imagery
Image Quality
High-resolution RAW photography
Processing
Professionally colour graded and processed for dataset consistency
Style
Consistent composition and lighting with candid and lightly directed portrait capture
Metadata Source
Mixture of human-authored and automatically generated standard stock metadata
Licensing, Documentation, and Delivery
Wavebreak Media can provide applicable participant-consent, provenance, licensing, and metadata information for the Diverse Facial Image Dataset, with dataset delivery organized around the project requirement. See our Dataset Licensing & Compliance and Dataset Delivery & Security pages for details on rights documentation, packaging, and secure transfer.
Need the Full Dataset?
Request access to the Diverse Facial Image Dataset or define the required mix of subjects, age coverage, expression conditions, occlusion levels, metadata fields, and delivery format for your face-focused AI workflow.
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