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

arXiv: https://arxiv.org/abs/2406.19672 · HTML: https://mlsanity.com/paper/2406.19672/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 28 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Rotate to Attend: Convolutional Triplet Attention Module](https://mlsanity.com/paper/2010.03045/) (2020)
- [Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification](https://mlsanity.com/paper/2308.00451/) (2023)
- [Scale-aware competition network for palmprint recognition](https://mlsanity.com/paper/2311.11354/) (2023)

## Abstract (arXiv)

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.