---
title: "Forensic Fingerprint Dataset"
description: "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,…"
url: "https://unidata.pro/datasets/forensic-fingerprint-dataset/"
date_modified: "2026-02-18T15:08:14+03:00"
language: "en-US"
---
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.

## Dataset Structure

### The Numbers Section

**Numbered list:**

- **Number:** 100 — **Text:** People
- **Number:** 6000+ — **Text:** Photos

### Tooltips Section

**Tooltip items:**

- **Name:** Computer Vision
- **Name:** Forensics
- **Name:** Machine Learning
- **Name:** Data Labeling

### Dataset Information

**Table with data:**

| 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) |

**Media Slider:**

- **Image in the slider:** ![forensic fingerprint dataset](https://unidata.pro/wp-content/uploads/2025/04/forensic-dataset.webp)
- **Image in the slider:** ![forensic fingerprint dataset](https://unidata.pro/wp-content/uploads/2025/04/forensic-dataset2.webp)
- **Image in the slider:** ![forensic fingerprint dataset](https://unidata.pro/wp-content/uploads/2025/04/forensic-dataset3.webp)
- **Image in the slider:** ![forensic fingerprint dataset](https://unidata.pro/wp-content/uploads/2025/04/forensic-dataset4.webp)
- **Image in the slider:** ![forensic fingerprint dataset](https://unidata.pro/wp-content/uploads/2025/04/forensic-dataset5.webp)

**Link to the sample:** [Download sample](https://drive.google.com/drive/folders/1P53aySeSacVte-z_E1Z9nmWSSbWDp3Dv)

### Technical Specifications

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Image Extensions | PNG, BMP |
| Device | ZKTeco ZK9500 |

**Source and data collection methodology:** Source and collection methodology: Data was collected using a ZKTeco ZK9500 optical scanner.

### Dataset Use Cases - Slider

**Industry Cards:**

- **Industry:** Security — **Title:** Robust Authentication for Consumer Devices — **Text:** This forensic fingerprint dataset supports security teams in improving fingerprint sensors used in smartphones, laptops, and access control systems. The fingerprint image dataset includes varied fingerprint patterns and quality levels, allowing recognition algorithms to handle smudged, partial, or low-contrast inputs. It strengthens fingerprint identification under real-world conditions common in everyday device use.
- **Industry:** Liveness Detection — **Title:** Liveness Detection Against Spoofing Attacks — **Text:** For liveness detection, fingerprint dataset helps identify synthetic and spoofed prints based on distortion patterns and texture inconsistencies. By training on biometric data that reflects realistic attack scenarios, security systems learn to separate real fingerprints from fake impressions. This improves resistance to presentation attacks and strengthens authentication reliability.
- **Industry:** Government and Border Control — **Title:** Visa and Passport Finger Processing at Scale — **Text:** Border control agencies use this fingerprint database to improve high-throughput identification systems. The forensic dataset supports fingerprint recognition when scans are noisy, incomplete, or captured under time pressure. It helps identification systems process large volumes of travelers while maintaining accuracy, supporting national databases, and automated fingerprint identification systems.
- **Industry:** Government and Border Control — **Title:** Disaster Victim Identification and Recovery — **Text:** In disaster victim identification, this dataset can help match compromised or partial prints collected from difficult environments. The database contains fingerprint images suitable for forensic investigations where traditional biometric signals are degraded. It supports forensic scientists and law enforcement during scene investigations, accelerating identification processes when time and accuracy are critical.

### Fact

**FAQs Heading:** FAQs

**List of Questions:**

- **Question:** What types of annotations are provided? — **Answer:** 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.
- **Question:** Can I request a sample of the dataset before purchasing? — **Answer:** 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.
- **Question:** How are Unidata datasets licensed? — **Answer:** 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.
- **Question:** How are Unidata datasets stored? — **Answer:** 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.
- **Question:** How long does it take to receive the dataset? — **Answer:** 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.
- **Question:** How was the data collected? — **Answer:** 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.
- **Question:** Is this a real-world dataset or synthetic data? — **Answer:** 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.
- **Question:** Can this dataset be used to improve fingerprint matching accuracy? — **Answer:** 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.

[Full list of this site's AI-readable pages](https://unidata.pro/llms.txt)