UniDataPro/biometric-fingerprint-spoofing

Fingerprint Spoofing

The dataset comprises 5,000+ high-quality fingerprint images collected from 100 individuals (ten fingers per person) captured using multiple fingerprint scanners and biometric sensors. 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.

By utilizing this data, researchers and developers can advance their understanding and capabilities in detecting spoofing attempts and enhancing user authentication technologies. - Get the data

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Example of the data

Researchers can utilize this dataset to explore detection algorithms and pattern recognition methods that aim to prevent fake biometric attempts and improve authentication processes in biometric systems.

Frequently Asked Questions

What is this fingerprint spoofing dataset used for?

This fingerprint dataset is designed for training and evaluating AI models for fingerprint spoofing, liveness detection, biometric authentication, and presentation attack detection (PAD). It helps researchers and developers improve fingerprint recognition systems by distinguishing genuine fingerprints from spoofing attempts.

What types of fingerprint spoofing attacks are included?

The dataset contains three labeled fingerprint presentation scenarios: real, print, and replay. These samples enable researchers to develop and evaluate AI models capable of distinguishing genuine fingerprints from common fingerprint spoofing attacks used to bypass biometric authentication systems.

Who can benefit from this fingerprint dataset?

This dataset is valuable for biometric security companies, AI researchers, authentication platform developers, fingerprint scanner manufacturers, cybersecurity teams, financial institutions, government agencies, and academic researchers working on fingerprint recognition and anti-spoofing technologies.

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