Palmprint recognition, as a biometric technology, is highly valued for its uniqueness and stability. This paper integrates traditional palmprint recognition techniques with deep learning-based methods, proposing a palmprint texture fusion ViT (PTF-ViT), aimed at enhancing the accuracy and robustness of palmprint recognition. We leverage the advantages of the Vision Transformer (ViT) to fuse the texture features of palmprint with original image information through an attention mechanism, thereby enhancing the model’s discriminative ability. Experimental results indicate that PTF-ViT performs exceptionally well across various palmprint datasets, exhibiting high Average Recognition Rate (ARR) and low Equal Error Rate (EER), especially demonstrating strong robustness on cross-device datasets. Compared to traditional methods and existing deep learning approaches, PTF-ViT holds significant advantages in the field of palmprint recognition, proving its effectiveness and advancement. This study not only fills the void of ViT in palmprint recognition but also paves a new direction for future research in biometric technology.