Touchless Fingerprint Capturing from RGB-D Images in Mobile Devices

Jonietz, Christof; Jivet, I.

doi:10.1109/isetc.2018.8583879

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…• Image depth information: Jonietz and Jivet [58] presented a segmentation approach using the information of a depth sensor combined with an RGB image captured by smartphones. The authors were able to extract the slap hand from a busy background and proposed further processing.…”

Section: Finger Detection and Segmentationmentioning

confidence: 99%

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An Overview of Touchless 2D Fingerprint Recognition

Rathgeb,

Priesnitz

2020

Preprint

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Touchless fingerprint recognition represents a rapidly growing field of research which has been studied for more than a decade. Through a touchless acquisition process, many issues of touch-based systems are circumvented, e.g., the presence of latent fingerprints or distortions caused by pressing fingers on a sensor surface. However, touchless fingerprint recognition systems reveal new challenges. In particular, a reliable detection and focusing of a presented finger as well as an appropriate preprocessing of the acquired finger image represent the most crucial tasks. Also, further issues, e.g., interoperability between touchless and touch-based fingerprints or presentation attack detection, are currently investigated by different research groups. Many works have been proposed so far to put touchless fingerprint recognition into practice. Published approaches range from self identification scenarios with commodity devices, e.g., smartphones, to high performance on-the-move deployments paving the way for new fingerprint recognition application scenarios. This work summarizes the state-of-the-art in the field of touchless 2D fingerprint recognition at each stage of the recognition process. Additionally, technical considerations and trade-offs of the presented methods are discussed along with open issues and challenges. An overview of available research resources completes the work.

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Section: Finger Detection and Segmentationmentioning

confidence: 99%

“…Setups designed for the usage under different environmental influence could also benefit from the use of depth information on an image like suggested by Jonitz and Jivet [58]. The additional depth information helps algorithms to segment the finer and gives a hint on the distance between finger and sensor.…”

Section: Environmental Influencesmentioning

confidence: 99%

An Overview of Touchless 2D Fingerprint Recognition

Rathgeb,

Priesnitz

2020

Preprint

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“…The local conditions like sensor efficiency, optimality, sample distortion, scratches and humidity etc. often impact efficacy of the classical touch-based fingerprint techniques [3] [7]. To alleviate such issues, improving feature modality in conjunction with contactless identification system seems to be the motivation for academiaindustry for future efforts [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…In the last few years very few but significant efforts have been made towards touchless fingerprint detection methods. Jonietz et al [3] recently tried to use depth camera and mobile www.ijacsa.thesai.org devices to perform touchless fingerprint detection. Despite RGB image and depth information combination, the key problem of non-uniform cues over feature space makes it limited towards realistic purposes.…”

Section: Introductionmentioning

confidence: 99%

Local Pre-Conditioning and Quality Enhancement to Handle Different Data Complexities in Contactless Fingerprint Classification

Deepika¹,

Shivakumar²

2022

IJACSA

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Biometric authentication systems have always been a fascinating approach to meet personalized security. Among the major existing solutions fingerprint-biometrics have gained widespread attention; yet, guaranteeing scalability and reliability over real-time demands remains a challenge. Despite innovations, the recent COVID-19 pandemic has capped the efficacy of the existing touch-based two-dimensional fingerprint detection models. Though, touchless fingerprint detection is considered as a viable alternative; yet, the real-time data complexities like nonlinear textural patterns, dusts, non-uniform local conditions like illumination, contrast, orientation make it complex for realization. Moreover, the likelihood of ridge discontinuity and spatiotemporal texture damages can limit its efficacy. Considering these complexities, here, we focused on improving the input image intrinsic feature characteristics. More specifically, applied normalization, ridge orientation estimation, ridge frequency estimation, ridge masking and Gabor filtering over the input touchless fingerprint images. The proposed model mainly focusses on reducing FPR & EER by dividing the input image in to blocks and classify each input block as recoverable and nonrecoverable image block. Finally, an image with higher recoverable blocks with sufficiently large intrinsic features were considered for feature extraction and classification. 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, specificity of 95.38% and a reduced EER of about 0.084.

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