Palmprint recognition systems experience a significant performance decline in cross-domain scenarios due to domain shift caused by non-identity factors such as capture devices and lighting conditions. To address this issue, this paper introduces a novel deep decoupling framework, the Identity and Style Feature Decoupling Network (ISFDNet), designed to improve the model’s cross-domain generalization. ISFDNet explicitly separates stable identity-related information from variable domain-related style information within palmprint features. The framework incorporates two innovative mechanisms: at the feature level, the Spatially-Aware Separation Module (SASM) adaptively produces complementary spatial attention masks to decouple mixed features into identity and style components; at the image level, the Low-Frequency Disturbance Module (LFDM) creates stylized training samples by perturbing the low-frequency parts of images, encouraging the network to learn identity representations that are insensitive to style variations. Additionally, a carefully designed collaborative supervision strategy combines multiple losses to ensure effective decoupling. Extensive experiments on four publicly available palmprint datasets demonstrate that ISFDNet achieves top performance in both cross-domain and in-domain tests, while significantly enhancing the generalization capabilities of existing networks. The code is released at https://github.com/20201422/ISFDNet.

Currently global features are usually extracted directly from local patterns in palmprint verification. Furthermore, sequence features for palmprint verification are only used as local features, but the properties of sequence features are not fully utilized. To solve this issue, this paper introduces Sequence Feature Fusion Network (SF2Net) for palmprint verification. SF2Net proposes a new paradigm: using stable and spatially correlated sequence features as an intermediate bridge to generate robust global representations. SF2Net’s core mechanism is to first extract fine-grained local features that are then converted into sequence features by a sequence feature extractor (SFE). Finally, the sequence features are used as a superior input to capture high-quality global features. By fusing multi-order texture-based local features with globally extracted sequence features, SF2Net achieves superior discrimination. To ensure high accuracy even with limited training data, a hybrid loss function is proposed, which integrate a cross-entropy loss and a triplet loss. Triplet loss effectively optimizes feature separation by explicitly considering negative samples. Extensive experiments on multiple publicly available palmprint datasets demonstrate that SF2Net achieves state-of-the-art (SOTA) performance. Remarkably, even with a small training-to-testing ratio (1:9), SF2Net achieves 100% accuracy, surpassing SOTA methods under several benchmark datasets. The code is released at https://github.com/20201422/SF2Net.