---
title: "Multi-Material Fingerprint Spoofing Dataset"
description: "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,…"
url: "https://unidata.pro/datasets/multi-material-fingerprint-spoofing/"
date_modified: "2026-04-09T12:33:51+03:00"
language: "en-US"
---
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.

## Dataset Structure

### The Numbers Section

**Numbered list:**

- **Number:** 100 — **Text:** People
- **Number:** 4000+ — **Text:** Photos

### Tooltips Section

**Tooltip items:**

- **Name:** Computer Vision
- **Name:** Machine Learning
- **Name:** Image Processing
- **Name:** Security
- **Name:** Anti-Spoofing

### Dataset Information

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Description | Fingerprint images were captured via four attack types: alginate, real, plasticine, and silicone |
| Data types | Image |
| Tasks | Spoof Detection, Classification |
| Number of images | 4000+ |
| Number of files in a set | 40 (10 real, 10 alginate, 10 plasticine, 10 silicone) |
| Type of attack | Alginate, real, plasticine, and silicone. |
| Labeling | Metadata (gender, age, finger, hand, device) |
| Total number of people | 100 |

**Media Slider:**

- **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2026/03/multi-material-fingerprint-dataset-cover-example.webp)
- **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2026/03/fingerprint-dataset-cover-example2.webp)

**Link to the sample:** [Download sample](https://drive.google.com/drive/folders/1YdWludVpDj9Vjwt9_qu21Yi7EaXzvWDy)

### Technical Specifications

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Image Extension | JPG |
| 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:** Biometric Security — **Title:** Fingerprint Spoof Detection for Authentication Systems — **Text:** Biometric security platforms rely on accurate spoof detection to prevent unauthorized access. This fingerprint dataset contains real and fake fingerprint images produced with alginate, plasticine, and silicone materials. The variety of attack types allows developers to train detection algorithms that identify presentation attacks in fingerprint scanners and biometric authentication systems.
- **Industry:** Cybersecurity & Identity Protection — **Title:** Presentation Attack Detection in Biometric Systems — **Text:** Security researchers can use this fingerprint dataset to study presentation attack detection in modern biometric systems. The database contains labeled fingerprint images and spoofing materials that simulate real attack scenarios. Such training data helps improve biometric antispoofing algorithms used in identity verification, access control technologies, and secure authentication platforms.
- **Industry:** Computer Vision & Deep Learning — **Title:** Training Deep Learning Models for Fingerprint Analysis — **Text:** Computer vision teams apply this dataset when training deep learning models for fingerprint recognition and spoof detection. The collection includes multiple fingerprint types captured by optical scanners, providing consistent image data for model training. Researchers can evaluate recognition systems and improve algorithms that detect spoof fingerprints during authentication processes.
- **Industry:** Forensics & Biometric Research — **Title:** Fingerprint Identification and Pattern Analysis — **Text:** Forensic laboratories and biometric research groups analyze fingerprint images to study differences between genuine and spoof fingerprints. The dataset supports fingerprint analysis, fingerprint comparison, and pattern recognition tasks. Researchers use it to test detection methods, evaluate biometric identification systems, and improve reliability in real-world security and forensic investigations.

### Fact

**FAQs Heading:** FAQs

**List of Questions:**

- **Question:** What types of annotations are provided in the dataset? — **Answer:** Each fingerprint image includes structured metadata annotations such as gender, age, finger type, hand orientation, and capture device.
- **Question:** What biometric attack types are represented in the dataset? — **Answer:** The dataset includes four categories of fingerprint captures: real fingerprints, alginate spoofs, plasticine spoofs, and silicone spoofs. These different attack types allow researchers to test spoofing detection algorithms across multiple biometric attack scenarios.
- **Question:** How was the data collected? — **Answer:** Fingerprint images were captured using a ZKTeco ZK9500 optical fingerprint scanner in controlled conditions.
- **Question:** Why does the dataset include multiple fingerprint spoofing materials? — **Answer:** Using multiple spoofing materials helps AI models learn to distinguish genuine fingerprints from different presentation attack techniques. This improves the robustness of biometric authentication systems against a wider range of real-world spoof detection scenarios.
- **Question:** How are Unidata datasets licensed? — **Answer:** Unidata datasets follow a dual-licensing model. Free samples are available for testing and evaluation, while full datasets are provided exclusively through purchase.
- **Question:** Do Unidata datasets comply with GDPR and other data privacy regulations? — **Answer:** Yes. All datasets are curated in accordance with GDPR and applicable data protection laws. Data is collected from legally permissible sources to ensure ethical and lawful usage.
- **Question:** How are Unidata datasets stored? — **Answer:** All datasets are securely stored on AWS cloud infrastructure, ensuring high availability and scalability. Storage practices follow ISO 27001 and ISO 27701 standards, providing internationally recognized information security and privacy management.
- **Question:** How long does it take to receive the dataset? — **Answer:** After submitting a request, the Unidata team reviews the dataset requirements and prepares the necessary documentation. Once the agreement is signed and payment is completed, the dataset is delivered within 3–10 days.

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