Mobile Touchless Fingerprint Acquisition And Enhancement System

Sağıroğlu, Şeref; Ulker, Mehtap; Arslan, Bilgehan

doi:10.1109/cec48606.2020.9185870

Cited by 4 publications

(8 citation statements)

References 21 publications

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“…A touchless system would mitigate these issues. We note the development of large field of view quadratic metalenses allowing the acquisition of 5 mm fingerprint with features of the order of 100 μm at a 2.5 mm distance [ 117 ] and the search for algorithm allowing the capture of marks at a distance with mobile devices [ 118 ]. The application of NFIQ2 as a quality metric for touchless system has been shown [ 119 ].…”

Section: Friction Ridge Skin and Its Individualization Processmentioning

confidence: 99%

Interpol review of fingermarks and other body impressions (2019 – 2022)

Bécue

Champod

2023

Forensic Science International: Synergy

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Section: Friction Ridge Skin and Its Individualization Processmentioning

confidence: 99%

Interpol review of fingermarks and other body impressions (2019 – 2022)

Bécue

Champod

2023

Forensic Science International: Synergy

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“…Khan et al addressed rotation and scale-invariant images acquired through smartphones [ 11 ]. Sagiroglu et al proposed an apparatus for fingerprint identification using smartphones [ 9 ]. Though all these authors have worked on images acquired through camera scanners, they did not work on children’s fingerprints.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These studies used Commercial off the Shelf (COTS) scanners, fingerprint sensors, and apparatus with light diffuser for image acquisition. Contact-based acquisition systems can produce samples with distortions due to finger pressure, excessive sweat, similar skin conditions, and wounds [ 9 ]. Moreover, contact-based systems leave a latent fingerprint on the scanner surface leading to security issues [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…In this digital era, smartphones are helpful for acquiring images as they are well equipped with high resolution camera and high-speed processing power. Several studies have been conducted for identification of adults using fingerprints acquired in a contactless way [ 9 , 11 – 14 ]. Convolutional Neural Network (CNN) based approaches yield good results in contactless fingerprint identification [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

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Convolutional neural network based children recognition system using contactless fingerprints

Rajaram¹,

Amma

Selvakumar

2023

Int. j. inf. tecnol.

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Biometric features are useful for unique identification, authentication, and security applications. Among all biometric features, fingerprints are the most commonly used because they contain ridges and valleys. There are challenges in recognizing child or infant fingerprints since the ridges are not mature as the hands are covered with a white substance and acquisition of fingerprint images is difficult. In the context of COVID-19 pandemic, contactless fingerprint acquisition gains importance as it is not infectious especially with children. In this study, a Convolutional Neural Network (CNN) based children recognition system named Child-CLEF, that uses Contact-Less Children Fingerprint (CLCF) dataset acquired using a mobile phone-based scanner is proposed. The quality of captured fingerprint images is enhanced using a hybrid image enhancement method. Furthermore, the minutiae features are extracted using the proposed Child-CLEF Net model and the identification of children is made using a matching algorithm. The proposed system is tested with a self-captured children fingerprint dataset, CLCF and publicly available PolyU fingerprint dataset. It is found that the proposed system outperforms the existing fingerprint recognition systems in terms of accuracy and equal error rate.

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“…Considering above stated issues and scopes (6) in this research two techniques have been proposed first, extracting conventional structural feature say minutiae followed by classification by Structural Similarity Index Matching. Second, to cope up with touchless fingerprint identification system demands (7) , in this paper, the emphasis was made on multi-dimensional optimization including pre-processing, feature extraction and eventual learning model (8) .…”

Section: Introductionmentioning

confidence: 99%

A Study on Structural and Textural Feature Extraction for Contactless Fingerprint Classification

Deepika¹,

Shivakumar²

2022

IJST

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Objectives:The Recent COVID 19 Pandemic has capped the efficacy of the mostly used existing touch-based fingerprint detection. Hence, the main objective of this study is to develop a lightweight, robust and efficient touchless fingerprint identification model. To cope up with touchless environment demands, an emphasis is made on improvement in local conditions, feature modalities as well as learning environment. Methods: Considering these objectives, the focus of this study is on two different methods for classification of contactless fingerprint. In the first method structural features like minutiae details are extracted followed by classification by SSIM. In the second method GLCM textural features were extracted followed by classification using Random Forest algorithm. In the proposed method, performance assessment is done by considering data samples of 1000 random users that are collected from different benchmark databases like Hong Kong Polytechnic University 3D-fngerprint images Database Version 2.0, Touchless Fingerprint Database of IIT Bombay, IIT Kanpur, IIT Jodhpur. Findings: Though, touchless fingerprint detection is considered as a viable alternative; yet, the real-time data complexities like non-linear textural patterns, dusts, non-uniform local conditions like illumination, contrast, orientation make it complex for realization. Moreover, the likelihood of ridge discontinuity and texture damages can majorly limit its efficacy. Novelty: The proposed model mainly focusses on reducing Equal Error Rate and improving the accuracy of contactless fingerprint classification by extracting textural features rather just sticking to conventional structural feature-minutiae. The Proposed method outperforms when compared with the existing state of the art methods by achieving an accuracy of 94.72%, precision of 98.84%, recall of 97.716%, F-Measure 0.9827 and a reduced EER of about 0.084. The key novelty of this approach was that it doesn't require any surface 3D reconstruction, rather it employed different mathematical approaches to retrieve surface normal and minutiae information.

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Mobile Touchless Fingerprint Acquisition And Enhancement System

Cited by 4 publications

References 21 publications

Interpol review of fingermarks and other body impressions (2019 – 2022)

Interpol review of fingermarks and other body impressions (2019 – 2022)

Convolutional neural network based children recognition system using contactless fingerprints

A Study on Structural and Textural Feature Extraction for Contactless Fingerprint Classification

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