Beyond First-Order: A Multi-Scale Approach to Finger Knuckle Print Biometrics

TL;DR

This paper presents DOTCNet, a multi-scale deep learning model that captures both first and second-order textures in finger knuckle print images, significantly improving recognition accuracy by incorporating structural information.

Contribution

The paper introduces a novel dual-order texture recognition network with learnable Gabor filters and attention mechanisms, advancing FKP recognition by integrating multi-scale texture features.

Findings

DOTCNet outperforms existing algorithms on PolyU-FKP dataset.

Incorporating second-order textures improves structural feature recognition.

Attention mechanisms enhance relevant texture features.

Abstract

Recently, finger knuckle prints (FKPs) have gained attention due to their rich textural patterns, positioning them as a promising biometric for identity recognition. Prior FKP recognition methods predominantly leverage first-order feature descriptors, which capture intricate texture details but fail to account for structural information. Emerging research, however, indicates that second-order textures, which describe the curves and arcs of the textures, encompass this overlooked structural information. This paper introduces a novel FKP recognition approach, the Dual-Order Texture Competition Network (DOTCNet), designed to capture texture information in FKP images comprehensively. DOTCNet incorporates three dual-order texture competitive modules (DTCMs), each targeting textures at different scales. Each DTCM employs a learnable texture descriptor, specifically a learnable Gabor filter (LGF), to extract texture features. By leveraging LGFs, the network extracts first and second order textures to describe fine textures and structural features thoroughly. Furthermore, an attention mechanism enhances relevant features in the first-order features, thereby highlighting significant texture details. For second-order features, a competitive mechanism emphasizes structural information while reducing noise from higher-order features. Extensive experimental results reveal that DOTCNet significantly outperforms several standard algorithms on the publicly available PolyU-FKP dataset.