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, includi...

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Bibliographic Details
Published in:IEEE transactions on biometrics, behavior, and identity science behavior, and identity science, 2024-01, Vol.6 (1), p.30-42
Main Authors: Grosz, Steven A., Jain, Anil K.
Format: Article
Language:English
Subjects:
attention
Biometric recognition systems
Computer vision
Convolutional neural networks
cross-sensor fingerprint recognition
Datasets
Deep learning
Feature extraction
Feature maps
Fingerprint embeddings
Fingerprint recognition
Fingerprint verification
fixed-length fingerprint representations
Realignment
sensor interoperability
Transformers
universal representation
vision transformers
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Summary: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.
ISSN:2637-6407
2637-6407
DOI:10.1109/TBIOM.2023.3317303