A Performance Evaluation of Transformer and Mamba Models for Palmprint and Palm Vein Recognition

Palmprint and palm vein recognition are vital biometric technologies for secure authentication. The primary challenge lies in modeling the complex, unstructured textures of palmprints and the continuous vascular topology of palm veins, both of which require capturing long-range spatial dependencies. Traditional handcrafted and CNN-based methods partially address these challenges but are constrained by local receptive fields and manually designed features. In contrast, Transformers leverage global self-attention to model cross-scale correlations in unstructured palmprint patterns, while Mamba state space models (SSMs) offer linear computational complexity and hardware efficiency, making them well-suited for capturing continuous topological structures in palm veins. We present the first systematic evaluation of Transformer and Mamba architectures for palmprint and palm vein recognition, covering 13 public datasets (eight palmprint, five palm vein) and 17 deep models (nine Transformer variants, eight Mamba architectures), alongside traditional methods and CNN baselines. Key findings include: (i) XCiT achieves the best palmprint recognition (open-set ACC = 95.31%, EER = 1.75%) via cross-covariance attention; (ii) MambaVision achieves the best palm vein recognition (open-set ACC = 91.25%, TAR@FAR [Formula: see text] = 76.81%) via hybrid CNN–SSM design; (iii) synthetic data augmentation improves few-shot performance by up to 12.6% ACC on challenging datasets while exhibiting boundary effects on high-quality data. We further quantify the accuracy–efficiency trade-off, offering practical deployment insights and highlighting the complementary roles of Transformer and Mamba architectures.