  The use of vision transformers (ViT) in computer vision is increasing due to
limited inductive biases (e.g., locality, weight sharing, etc.) and increased
scalability compared to other deep learning methods. 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 for
transformers in fingerprint recognition 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 transformer and CNN-based models,
including a SOTA commercial fingerprint system, 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 across each of the models. 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.
