Mobile Contactless Palmprint Recognition: Use of Multiscale, Multimodel
  Embeddings
Steven A. Grosz Akash Godbole Anil K. Jain
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.