Mobile Contactless Palmprint Recognition: Use of Multiscale, Multimodel Embeddings

• URL: http://arxiv.org/abs/2401.08111v1
• Date: Tue, 16 Jan 2024 04:42:54 GMT
• Title: Mobile Contactless Palmprint Recognition: Use of Multiscale, Multimodel Embeddings
• Authors: Steven A. Grosz, Akash Godbole and Anil K. Jain
• Abstract summary: 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. Palm-ID balances the trade-off between accuracy and latency, requiring just 18ms to extract a template of size 516 bytes.
• Score: 26.774583290826193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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