PalmMamba: Palm Intrinsic Features Learning Selective State Space Model for Palmprint Image Denoising

Palmprint-based biometric recognition has gained widespread attention due to its rich features, contactless acquisition, and low invasiveness. However, most existing methods neglect image quality, making them less effective for low-quality, noisy palmprint images. In this paper, we propose a palm intrinsic features learning selective state space model (PalmMamba) for palmprint image denoising, which consists of shallow feature representation, noise-insensitive palmprint-specific feature learning, and sharp palmprint image restoration modules. First, we convert the degraded noisy palmprint image into a high-dimensional shallow feature representation through a single-layer convolution backbone. Then, we develop parallel learning branches, including a second-order attention-based selective state space model and a mixed difference convolution module, to exploit diverse palmprint-specific features with both global and local details. Finally, we map the fine-grained palmprint-intrinsic feature map into the identity-preserved sharp palmprint image via a commonly used convolution layer. Extensive experimental results on five public palmprint databases demonstrate the encouraging performance of the proposed PalmMamba in palmprint image denoising.