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.

翻译：近年来，指关节纹因其丰富的纹理模式而受到关注，成为一种前景广阔的身份识别生物特征。现有的指关节纹识别方法主要利用一阶特征描述符，这些描述符能够捕捉精细的纹理细节，但未能考虑结构信息。然而，新兴研究表明，描述纹理曲线与弧度的二阶纹理包含了这些被忽视的结构信息。本文提出了一种新颖的指关节纹识别方法——双阶纹理竞争网络，旨在全面捕捉指关节纹图像中的纹理信息。DOTCNet包含三个双阶纹理竞争模块，每个模块针对不同尺度的纹理。每个DTCM采用一个可学习的纹理描述符，具体为可学习的Gabor滤波器，以提取纹理特征。通过利用LGF，网络提取一阶和二阶纹理，以彻底描述精细纹理和结构特征。此外，注意力机制增强了一阶特征中的相关特征，从而突出了重要的纹理细节。对于二阶特征，竞争机制强调结构信息，同时减少来自高阶特征的噪声。大量实验结果表明，在公开的PolyU-FKP数据集上，DOTCNet显著优于多种标准算法。