Improving fingerprint presentation attack detection by an approach integrated into the personal verification stage

2025-04-15

AI Summary

Existing fingerprint presentation attack detection (PAD) systems are typically decoupled from the verification module, limiting their ability to protect user-specific templates robustly. Method: This paper deeply integrates PAD into the verification pipeline by leveraging the hierarchical clustering structure—termed “closeness”—of genuine fingerprints in the embedding space, enabling robust attack detection without requiring target-user attack samples. The core innovation is the Closeness Binary Code (CBC), a lightweight plug-in module that jointly analyzes handcrafted and deep features via Euclidean distance, and generates binary codes through statistical hypothesis testing to model local neighborhood consistency of legitimate templates. Results: Evaluated on multiple benchmark datasets, CBC achieves significant improvements over state-of-the-art methods while maintaining real-time inference, strong generalization across sensors and materials, and deployment-friendly efficiency.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

Presentation Attack Detection (PAD) systems are usually designed independently of the fingerprint verification system. While this can be acceptable for use cases where specific user templates are not predetermined, it represents a missed opportunity to enhance security in scenarios where integrating PAD with the fingerprint verification system could significantly leverage users' templates, which are the real target of a potential presentation attack. This does not mean that a PAD should be specifically designed for such users; that would imply the availability of many enrolled users' PAI and, consequently, complexity, time, and cost increase. On the contrary, we propose to equip a basic PAD, designed according to the state of the art, with an innovative add-on module called the Closeness Binary Code (CC) module. The term"closeness"refers to a peculiar property of the bona fide-related features: in an Euclidean feature space, genuine fingerprints tend to cluster in a specific pattern. First, samples from the same finger are close to each other, then samples from other fingers of the same user and finally, samples from fingers of other users. This property is statistically verified in our previous publication, and further confirmed in this paper. It is independent of the user population and the feature set class, which can be handcrafted or deep network-based (embeddings). Therefore, the add-on can be designed without the need for the targeted user samples; moreover, it exploits her/his samples'"closeness"property during the verification stage. Extensive experiments on benchmark datasets and state-of-the-art PAD methods confirm the benefits of the proposed add-on, which can be easily coupled with the main PAD module integrated into the fingerprint verification system.

Problem

• Enhancing fingerprint security by integrating PAD with verification
• Leveraging user templates to improve attack detection accuracy
• Utilizing closeness property of genuine fingerprints for PAD

Innovation

• Integrated PAD with fingerprint verification system
• Closeness Binary Code module enhances security
• Exploits Euclidean feature space clustering property