“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
Forensic Fingerprint Dataset
This fingerprint database contains images collected from 100 individuals, with samples covering both hands and 10 fingers per person. It is designed for forensic matching, denoising, and minutiae extraction tasks and includes metadata such as gender, age, finger type, hand orientation. All images were captured by a ZKTeco ZK9500 optical scanner and are provided in PNG/BMP formats, making it well-suited for biometric identification and security research.
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- People
- 100
-
- Photos
- 6000+
- Computer Vision
- Forensics
- Machine Learning
- Data Labeling
- Computer Vision
- Forensics
- Machine Learning
- Data Labeling
This fingerprint database contains images collected from 100 individuals, with samples covering both hands and 10 fingers per person. It is designed for forensic matching, denoising, and minutiae extraction tasks and includes metadata such as gender, age, finger type, hand orientation. All images were captured by a ZKTeco ZK9500 optical scanner and are provided in PNG/BMP formats, making it well-suited for biometric identification and security research.
- Computer Vision
- Forensics
- Machine Learning
- Data Labeling
-
- People
- 100
-
- Photos
- 6000+
Dataset Info
| Characteristic | Data |
| Description | Fingerprint images for forensic matching and denoising tasks |
| Data types | Image |
| Tasks | Matching, denoising, minutiae extraction |
| Number of images | 6000+ |
| Number of files in a set | 10 (five fingers per hand) |
| Total number of people | 100 |
| Labeling | Metadata (gender, age, finger, hand) |
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Technical
Characteristics
| Characteristic | Data |
| Image Extensions | PNG, BMP |
| Device | ZKTeco ZK9500 |
Dataset Use Cases
FAQs
What types of annotations are provided?
The fingerprint image dataset is annotated with structured metadata, including gender, age, hand (left/right), and finger type. These annotations support biometric analysis, fingerprint classification, and supervised machine learning workflows without relying on latent-to-reference correspondence labels.
Can I request a sample of the dataset before purchasing?
Yes. You can request a free sample to evaluate the quality of fingerprint images, annotation accuracy, and variation in latent prints. This helps verify compatibility with your forensic recognition systems or AI fingerprint classification models before making a full purchase.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model: free samples are offered for evaluation, while full datasets are available exclusively through purchase. This ensures flexible access for both research and enterprise use.
How are Unidata datasets stored?
All datasets are securely hosted on AWS cloud infrastructure, following ISO 27001 and ISO 27701 standards. This guarantees high data availability, secure access, and privacy compliance for all dataset transactions and storage.
How long does it take to receive the dataset?
Once your request is submitted, the Unidata team will contact you to review details and complete the necessary documentation. After signing and payment, the dataset is delivered within 3–10 business days through a secure download link.
How was the data collected?
All data was collected through crowdsourcing platforms, involving volunteers who provided controlled and varied fingerprint impressions. This method ensures high diversity, realistic distortion patterns, and reliable forensic scenarios for both academic and professional research.
Is this a real-world dataset or synthetic data?
This is a real-world dataset, collected from human participants via crowdsourcing, not AI-generated. The dataset accurately reflects the complexity of real forensic evidence, including latent prints and environmental distortions.
Can this dataset be used to improve fingerprint matching accuracy?
Yes. The dataset supports the development of fingerprint matching models by providing multiple samples across different fingers and hands. This helps train algorithms to recognize unique fingerprint patterns and improve identification performance.
Still have questions about using Unidata datasets?
Read our user-guides
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