Deep Age-Invariant Fingerprint Segmentation System

Murshed, M. G. Sarwar; Bahmani, Keivan; Schuckers, Stephanie; Hussain, Faraz

doi:10.48550/arxiv.2303.03341

Cited by 1 publication

(4 citation statements)

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“…In prior work, we developed a two-stage Faster R-CNN architecture for segmenting contactbased slap fingerprints [15]. We utilized a similar two-stage faster architecture for segmenting contactless slap fingerphotos.…”

Section: Deep Learning Architecture For Contactless Fingerphoto Segme...mentioning

confidence: 99%

“…We have developed a deep learning-based fingerprint segmenter called CRFSEG [15] which outperforms other fingerprint segmenters in terms of precise fingerprint detection and fingerprint matching accuracy. However, it is important to note that these fingerprint segmenters have mainly been developed and used for contact-based slap fingerprint images.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we describe our novel contactless fingerprint segmentation system developed by enhancing our existing contact-based fingerprint segmentation model, CRFSEG (Clarkson Rotated Fingerprint Segmentation Model) [15], by updating its architecture and training it on a contactless dataset. Our novel contactless segmentation model CRFSEG-v2 demonstrates higher accuracy when evaluated on our in-house contactless fingerprint dataset consisting of 23,650 slaps, which were annotated by human experts.…”

Section: Introductionmentioning

confidence: 99%

“…We describe a novel model for segmentation of contactless fingerphotos. Its architecture is based on our prior fingerprint segmentation system [15]. Special attention was given to optimizing the deep learning architecture for this purpose.…”

Section: Introductionmentioning

confidence: 99%

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Deep Slap Fingerprint Segmentation for Juveniles and Adults

Murshed

Kline

Bahmani

et al. 2021

2021 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

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Fingerprints are widely recognized as one of the most unique and reliable characteristics of human identity. Most modern fingerprint authentication systems rely on contact-based fingerprints, which require the use of fingerprint scanners or fingerprint sensors for capturing fingerprints during the authentication process. Various types of fingerprint sensors, such as optical, capacitive, and ultrasonic sensors, employ distinct techniques to gather and analyze fingerprint data. This dependency on specific hardware or sensors creates a barrier or challenge for the broader adoption of fingerprint based biometric systems. This limitation hinders the widespread adoption of fingerprint authentication in various applications and scenarios. Border control, healthcare systems, educational institutions, financial transactions, and airport security face challenges when fingerprint sensors are not universally available.To mitigate the dependence on additional hardware, the use of contactless fingerprints has emerged as an alternative. Developing precise fingerprint segmentation methods, accurate fingerprint extraction tools, and reliable fingerprint matchers are crucial for the successful implementation of a robust contactless fingerprint authentication system. This paper focuses on the development of a deep learning-based segmentation tool for contactless fingerprint localization and segmentation. Our system leverages deep learning techniques to achieve high segmentation accuracy and reliable extraction of fingerprints from contactless fingerprint images. In our evaluation, our segmentation method demonstrated an average mean absolute error (MAE) of 30 pixels, an error in angle prediction (EAP) of 5.92 degrees, and a labeling accuracy of 97.46%. These results demonstrate the effectiveness of our novel contactless fingerprint segmentation and extraction tools.

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Section: Deep Learning Architecture For Contactless Fingerphoto Segme...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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