Palmprint recognition based on principal line features.

Abstract

With the increasing prevalence and diversity of imaging devices, palmprint recognition has emerged as a technology that better meets the demands of the modern era. However, traditional manual methods have limitations in effectively extracting palmprint principal line features. To address this, we introduce a novel data augmentation method. First, the wide line extraction (WLE) filter is utilized to specifically target and extract the prominent principal lines of palmprints by leveraging their direction and width characteristics. Then, a Gabor filter is applied to the WLE-extracted results to purify the features and remove fine lines, as fine lines can introduce noise and redundancy that interfere with the accurate extraction of significant principal line features crucial for palmprint recognition. Evaluating this data augmentation across four common Vision Transformer (ViT) classification models, experimental results show that it improves the recognition rates of all databases to varying degrees, with a remarkable 32.9% increase on the high-resolution XINHUA database. With the successful removal of fine lines by WLE, we propose a new Layer Visual Transformer (LViT) design paradigm. For its input, distinct blocking strategies are adopted, carefully designed to partition the data to capture different levels of spatial and feature information, using larger blocks for global structure and smaller ones for local details. The output results of these different blocking strategies are fused by "sum fusion" and "maximum fusion", and the local and global features are effectively utilized by combining complementary information to improve the recognition performance and get state-of-the-art results on multiple databases. Moreover, LViT requires fewer training iterations due to the synergistic effects of the blocking strategies, optimizing the learning process. Finally, by simulating real-world noise conditions, we comprehensively evaluate LViT and find that, compared with traditional methods, our approach exhibits excellent noise-resistant generalization ability, maintaining stable performance across the PolyU II, IIT Delhi, XINHUA, and NTU-CP-V1 databases.