  Contactless palmprints are comprised of both global and local discriminative
features. Most prior work focuses on extracting global features or local
features alone for palmprint matching, whereas this research introduces a novel
framework that combines global and local features for enhanced palmprint
matching accuracy. Leveraging recent advancements in deep learning, this study
integrates a vision transformer (ViT) and a convolutional neural network (CNN)
to extract complementary local and global features. Next, a mobile-based,
end-to-end palmprint recognition system is developed, referred to as Palm-ID.
On top of the ViT and CNN features, Palm-ID incorporates a palmprint
enhancement module and efficient dimensionality reduction (for faster
matching). Palm-ID balances the trade-off between accuracy and latency,
requiring just 18ms to extract a template of size 516 bytes, which can be
efficiently searched against a 10,000 palmprint gallery in 0.33ms on an AMD
EPYC 7543 32-Core CPU utilizing 128-threads. Cross-database matching protocols
and evaluations on large-scale operational datasets demonstrate the robustness
of the proposed method, achieving a TAR of 98.06% at FAR=0.01% on a newly
collected, time-separated dataset. To show a practical deployment of the
end-to-end system, the entire recognition pipeline is embedded within a mobile
device for enhanced user privacy and security.
