AFR-Net: Attention-Driven Fingerprint Recognition Network

The use of vision transformers (ViT) in computer vision is increasing due to its limited inductive biases (e.g., locality, weight sharing, etc.) and increased scalability compared to other deep learning models. This has led to some initial studies on the use of ViT for biometric recognition, including fingerprint recognition. In this work, we improve on these initial studies by i.) evaluating additional attention-based architectures, ii.) scaling to larger and more diverse training and evaluation datasets, and iii.) combining the complimentary representations of attention-based and CNN-based embeddings for improved state-of-the-art (SOTA) fingerprint recognition (both authentication and identification). Our combined architecture, AFR-Net (Attention-Driven Fingerprint Recognition Network), outperforms several baseline models, including a SOTA commercial fingerprint system by Neurotechnology, Verifinger v12.3, across intra-sensor, cross-sensor, and latent to rolled fingerprint matching datasets. Additionally, we propose a realignment strategy using local embeddings extracted from intermediate feature maps within the networks to refine the global embeddings in low certainty situations, which boosts the overall recognition accuracy significantly. This realignment strategy requires no additional training and can be applied as a wrapper to any existing deep learning network (including attention-based, CNN-based, or both) to boost its performance in a variety of computer vision tasks.

I. Introduction

Automated fingerprint recognition systems have continued to permeate many facets of everyday life, appearing in many civilian and governmental applications over the last several decades [1]. As an example, India’s Aadhaar civil registration system is used to authenticate approximately 70 million transactions per day, primarily with fingerprints.1 Due to the impressive accuracy of fingerprint recognition algorithms (0.626% False Non-Match Rate at a False Match Rate of 0.01% on the FVC-ongoing 1:1 hard benchmark [2]), researchers have turned their attention to addressing difficult edge-cases where accurate recognition remains challenging, such as partial overlap between two candidate fingerprint images and cross-sensor interoperability (e.g., optical to capacitive, contact to contactless, latent to rolled fingerprints, etc.), as well as other practical problems like template encryption, privacy concerns, and matching latency for large-scale (gallery sizes on the order of tens or hundreds of millions) identification.¹

https://uidai.gov.in/aadhaar_dashboard/auth_trend.php