“We were very happy with the quality of the data that we purchased. It will assist our efforts to improve our product. The team at Unidata was very responsive to our needs, and provided the data format needed to complete our tasks. We are happy to state that Unidata delivers a high quality product in an effective and efficient manner.”
Commercial
Makeup Detection Dataset: Female Images
Makeup dataset including 5,000 paired JPG facial images (2,500 subjects) of women with and without makeup, captured using diverse devices and lighting conditions. Each image is annotated with metadata (ID, gender, age, country, device model), making it ideal for facial recognition, makeup detection, and training computer vision models.
-
- images
- 5,000
-
- people
- 2,500
- Computer Vision
- Machine Learning
- Facial Recognition
- Makeup Detection
- Computer Vision
- Machine Learning
- Facial Recognition
- Makeup Detection
Makeup dataset including 5,000 paired JPG facial images (2,500 subjects) of women with and without makeup, captured using diverse devices and lighting conditions. Each image is annotated with metadata (ID, gender, age, country, device model), making it ideal for facial recognition, makeup detection, and training computer vision models.
- Computer Vision
- Machine Learning
- Facial Recognition
- Makeup Detection
-
- images
- 5,000
-
- people
- 2,500
Dataset Info
| Characteristic | Data |
| Description | Paired facial images of females with and without makeup |
| Data types | Image |
| Tasks | Beauty AI, Computer Vision |
| Number of people | 2,500 |
| Number of files in a set | 2 images per person |
| Total number of files | 5,000 |
| Labeling | Metadata (ID, gender, age, country, device model) |
| Gender | Male, Female |
Fintech hit by 100K biometric spoofing attacks monthly
Discover how top fintechs prevent fraud — download our free white paper
Statistics
-
- Distribution by country
-
- Distribution by gender
Technical
Characteristics
| Characteristic | Data |
| Image extension | JPG |
Dataset Use Cases
FAQs
Can I request a sample of the dataset before purchasing?
Yes. Free dataset samples are available for testing and evaluation. These samples help you assess data quality, diversity, and relevance before committing to a full purchase.
How was the data collected?
The dataset was collected through crowdsourcing platforms, ensuring real-world diversity across different individuals, makeup styles, and lighting conditions. Participants provided paired facial images captured according to controlled guidelines to maintain consistency and balance between makeup and non-makeup samples.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model: free samples are available for testing, while full datasets require purchase for full access and commercial use. This ensures both accessibility for research and protection of dataset integrity.
Do Unidata datasets follow GDPR or other data privacy regulations?
Yes. All Unidata datasets comply with GDPR and relevant global data protection laws. Data is collected through lawful and ethical means, ensuring the privacy and informed consent of participants.
How are Unidata datasets stored?
All datasets are securely stored on AWS cloud infrastructure, which complies with ISO 27001 and ISO 27701 standards. This ensures data security, privacy, and high availability for all clients and partners.
How long does it take to receive the dataset?
Once you submit a request, our team will contact you to confirm details and complete documentation. After signing and payment, the dataset will be delivered within 3–10 business days.
Is this real-world or synthetic data?
This is a real-world dataset featuring genuine photographs of female subjects captured through verified crowdsourcing. The data reflects natural variations in makeup styles, lighting, and facial expressions - ideal for training robust recognition algorithms.
Why are paired images important for makeup detection research?
Paired images allow AI models to compare the same individual before and after makeup application without introducing identity-related differences. This makes it easier to isolate cosmetic changes and improve model performance.
Why is demographic metadata valuable for makeup detection models?
Metadata such as age, country, gender, and device model allows researchers to evaluate model performance across different user groups and image capture conditions. This helps develop fairer and more robust AI systems.
Still have questions about using Unidata datasets?
Read our user-guides
Unidata Cases
Digital Tree Passport Annotation for Forest Mapping
- Forestry Monitoring & GIS
- 200,000 trees, 10 species classes
- 2 months
License Plate Annotation for Vehicle Recognition System
- 100,000 images with detailed license plate markup (bounding boxes, digits, regional symbols)
- 3 weeks
Sentiment Annotation for Brand Monitoring
- Marketing & Consumer Insights
- 12,000 text samples
- 7 weeks
Surveillance Video Annotation for Entrance Monitoring
- Surveillance & Security
- 90 minutes of video from three cameras, approximately 50-60 thousand frames
- 2 week
Similar Datasets
-
Commercial
- Identity Verification
- Computer Vision
- Machine Learning
- Biometric Recognition
- Security
Human Iris Images Biometric Dataset
High-quality iris dataset with 5,000+ JPG images from 5,000+ individuals, containing left and right eye captures for iris recognition and computer vision tasks. This eye iris dataset includes labeled images collected under visible light, with metadata (ID, eye, sex) to support training data for biometric recognition systems, iris segmentation, and iris pattern analysis.
5,000+ Images
5,000+ People -
Commercial
- Computer Vision
- Machine Learning
- Image Processing
- Security
- Anti-Spoofing
Multi-Material Fingerprint Spoofing Dataset
This fingerprint spoofing dataset contains fingerprint images, captured with a ZKTeco ZK9500 optical scanner and including real fingerprints and spoofing attacks created with alginate, plasticine, and silicone materials. The fingerprint dataset includes metadata (gender, age, finger, hand, device) and supports biometric security research, presentation attack detection, spoof detection, and fingerprint recognition model training.
100 People
4000+ Photos -
Commercial
- Computer Vision
- Machine Learning
- Image Processing
- Security
- Anti-Spoofing
Biometric Fingerprint Spoofing Dataset
Contains 5,000+ high-quality fingerprint images capturing real fingerprints and multiple fingerprint spoofing attack types, including print and replay scenarios. Designed for spoofing detection and liveness detection tasks, the fingerprint dataset provides labeled biometric data from different devices and fingers to train and evaluate biometric security and fingerprint recognition systems.
100 People
5000+ Photos -
Commercial
- Facial Recognition
- Liveness Detection
- Security
- Anti-spoofing
- Computer Vision
Anti-Spoofing Replay PC Videos Dataset
High-quality replay attack dataset containing 4,714 PC-recorded video clips of real faces, designed for training and evaluating face recognition and liveness detection systems. This anti-spoofing videos dataset includes diverse attack scenarios, technical metadata (age, gender, ethnicity), and MP4/MOV formats to support spoofing detection, biometric security, and computer vision model development.
4,714 Videos
4,714 People -
Commercial
- Facial Recognition
- Liveness Detection
- Security
- Anti-spoofing
- Computer Vision
Anti-Spoofing Replay Phone Videos Dataset
This anti-spoofing dataset contains over 40,036 live facial video recordings captured on mobile devices to support replay attack detection and biometric anti-spoofing research. With paired video sets, MP4/MOV formats, and rich metadata such as age, gender, and ethnicity, it provides reliable training data for face antispoofing, liveness detection, and secure biometric authentication systems.
40,036 Videos
20,018 Sets -
Commercial
- Speech Analysis
- ASR
- Machine learning
- Data generation
- Audio Processing
Real vs Fake Human Voice – Deepfake Audio Dataset
Audio dataset containing 5,000 audio files featuring both genuine human recordings and AI-generated voice samples. Each set includes four speakers with multiple clips across M4A and MP3 formats. The dataset supports research in deepfake detection, generated speech analysis, and real vs fake human voice recognition tasks.
5,000 Audio
-
Commercial
- Facial Recognition
- Security
- Anti-spoofing
- Computer Vision
- Machine Learning
Kids Anti-Spoofing Dataset
The dataset provides 6,000 high-quality facial images of children aged 7–15 for face anti-spoofing and liveness detection tasks. This child safety dataset supports research in biometric systems, helping improve facial recognition accuracy, detect spoofing attacks, and build safer AI models for protecting kids in digital and identification environments.
6 000 Images
300 people -
Commercial
- Image Processing
- Machine Learning
- Hand Recognition
- Forensics
- Computer Vision
Open Palm Hand Images Dataset
This high-quality open palm dataset includes 179,052 annotated images collected from 29,842 people, with each set containing six palm photos, two printed-hand images, and two replay videos. Designed for hand recognition and computer vision research, it provides detailed metadata - age, gender, ethnicity, profession, device type, dominant hand, and jewelry status.
179,052 Images
29,842 People -
Commercial
- PII
- Data generation
- Security
- Anti-spoofing
- Computer Vision
Synthetic Printed Turkish Passports Dataset
It is a synthetic Turkish passports dataset containing 5,000 high-quality, AI-generated images. Labeled with detailed metadata - including passport ID, class, gender, and lighting - this dataset supports PII extraction, identity verification, and biometric recognition system training while maintaining strict data protection standards.
5000 Images
-
Commercial
- Facial Recognition
- iBeta
- Liveness Detection
- Security
- Anti-spoofing
- Computer Vision
iBeta Kids Dataset
It is a child safety dataset featuring over 46,000 short video samples of children across different age groups, recorded under varied lighting, devices, and conditions. It includes four main attack types: Real Person, 2D Mask, 3D Mask, and Replay, helping develop biometric systems that detect spoofing and ensure safe, accurate child identification.
45 600 Videos
60 People
Why Companies Trust Unidata's Datasets
Share your project requirements, we handle the rest. Every service is tailored, executed, and compliance-ready, so you can focus on strategy and growth, not operations.
Our Clients Love Us
Ready to get started?
Tell us what you need — we’ll reply within 24h with a free estimate
- Andrew
- Head of Client Success
— I'll guide you through every step, from your first
message to full project delivery
Thank you for your
message
We use cookies to enhance your experience, personalize content, ads, and analyze traffic. By clicking 'Accept All', you agree to our Cookie Policy.