  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.
