Transformer based Fingerprint Feature Extraction

Tandon, Saraansh; Namboodiri, Anoop

doi:10.1109/icpr56361.2022.9956435

Cited by 10 publications

(1 citation statement)

References 21 publications

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“…Recent advancements in deep learning have witnessed the rise of vision transformer (ViT) architectures [22], rivaling convolutional neural networks (CNNs) for various computer vision applications, including biometrics [23], [24], [25]. Studies suggest that CNNs and ViTs encode different and complementary features for the same task [26], motivating a fusion of both architectures for improved accuracy [27], [28].…”

Section: B Our Work and Contributionsmentioning

confidence: 99%

C2CL: Contact to Contactless Fingerprint Matching

Grosz

Engelsma

Liu

et al. 2022

IEEE Trans.Inform.Forensic Secur.

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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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