Fixed-Length Dense Fingerprint Representation

2025-05-06

AI Summary

Existing methods struggle to simultaneously achieve representation robustness and computational efficiency for cross-modal and low-quality fingerprint matching. Method: This paper proposes FLARE—a novel framework featuring (i) the first fixed-length, spatially corresponded 3D dense fingerprint descriptor; (ii) multi-granularity pose-complementary estimation for geometric alignment; and (iii) a modality-preserving dual-path ridge enhancement network to suppress noise and modality discrepancies. Similarity is computed efficiently via dense vector inner products. Contributions/Results: FLARE achieves state-of-the-art performance across four fingerprint types—rolled, plain, latent, and contactless—particularly excelling in cross-modal and low-quality scenarios, with substantial gains in matching accuracy. It demonstrates strong generalization across diverse acquisition conditions and scalability to real-world deployment.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Fixed-length fingerprint representations, which map each fingerprint to a compact and fixed-size feature vector, are computationally efficient and well-suited for large-scale matching. However, designing a robust representation that effectively handles diverse fingerprint modalities, pose variations, and noise interference remains a significant challenge. In this work, we propose a fixed-length dense descriptor of fingerprints, and introduce FLARE-a fingerprint matching framework that integrates the Fixed-Length dense descriptor with pose-based Alignment and Robust Enhancement. This fixed-length representation employs a three-dimensional dense descriptor to effectively capture spatial relationships among fingerprint ridge structures, enabling robust and locally discriminative representations. To ensure consistency within this dense feature space, FLARE incorporates pose-based alignment using complementary estimation methods, along with dual enhancement strategies that refine ridge clarity while preserving the original fingerprint modality. The proposed dense descriptor supports fixed-length representation while maintaining spatial correspondence, enabling fast and accurate similarity computation. Extensive experiments demonstrate that FLARE achieves superior performance across rolled, plain, latent, and contactless fingerprints, significantly outperforming existing methods in cross-modality and low-quality scenarios. Further analysis validates the effectiveness of the dense descriptor design, as well as the impact of alignment and enhancement modules on the accuracy of dense descriptor matching. Experimental results highlight the effectiveness and generalizability of FLARE as a unified and scalable solution for robust fingerprint representation and matching. The implementation and code will be publicly available at https://github.com/Yu-Yy/FLARE.

Problem

• Designing robust fixed-length fingerprint representations for diverse modalities
• Handling pose variations and noise in fingerprint matching
• Achieving fast, accurate similarity computation in cross-modality scenarios

Innovation

• 3D dense descriptor captures ridge spatial relationships
• Pose-based alignment ensures feature space consistency
• Dual enhancement refines clarity and preserves modality