A local feature vector for an adaptive hybrid fingerprint matcher

Tran, Manh Hoang; Duong, Tan Nghia; Nguyen, Duc Minh; Dang, Quang Hieu

doi:10.1109/infoc.2017.8001668

Cited by 14 publications

(5 citation statements)

References 13 publications

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“…The proposed technique suffers from nonlinear distortion. The corresponding algorithm's nonlinear distorting condition requires a solution to the problem (Tran MH et al 2017), who suggested a matching process based on individual detail's local feature representation. There is increasing concern about the susceptibility of fingerprint protection to attacks.…”

Section: Conventional Techniques For Fingerprint Identificationmentioning

confidence: 99%

Survey on the convergence of learning techniques for precise pattern identification in fingerprint analysis

Agarwal,

Shrimal,

Shahid

et al. 2024

Multidiscip. Rev.

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Fingerprint analysis has been an essential component of forensic science and biometric authentication systems. The precise recognition of fingerprint patterns is critical for solving crimes, confirming identities and assuring the security of numerous applications. In recent years, the convergence of learning approaches, particularly machine learning and deep learning, has played an essential part in improving the accuracy and efficiency of fingerprint pattern recognition. In this paper, we discuss recent advancements in physiological-based biometric multimodalities, with a particular focus on the field of precise pattern identification in fingerprint analysis. Additionally, we assume the task of summarizing and examining a range of physiological-based biometric modalities, encompassing both traditional and deep learning approaches. A detailed review of several biometric measurements across many modalities is presented, encompassing various phases, including preprocessing, feature extraction and classification, which are thoroughly discussed. We provide a comprehensive analysis of the challenges and future developments associated with conventional and deep learning methodologies. The objective is to enable investigators to recognize these problems. A review is conducted to evaluate the standard and deep learning approaches employed in different physiological-based biometric systems. The comparative analysis of this review suggests that more advancement is necessary for the development of a reliable physiological-based approach to enhance and optimize the functionality of the fingerprint system.

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Section: Conventional Techniques For Fingerprint Identificationmentioning

confidence: 99%

Survey on the convergence of learning techniques for precise pattern identification in fingerprint analysis

Agarwal,

Shrimal,

Shahid

et al. 2024

Multidiscip. Rev.

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“…Another alignment invariant technique based on minutiae triplet is designed by Ahn et al [58]. Tran et al [59] have designed a method based on hybrid matcher for user authentication. Wang et al [60] used zoned minutia pair for local minutiae structure to design a scheme that computes cancelable user template.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimized Authentication System with High Security and Privacy

et al. 2021

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Authentication and privacy play an important role in the present electronic world. Biometrics and especially fingerprint-based authentication are extremely useful for unlocking doors, mobile phones, etc. Fingerprint biometrics usually store the attributes of the minutia point of a fingerprint directly in the database as a user template. Existing research works have shown that from such insecure user templates, original fingerprints can be constructed. If the database gets compromised, the attacker may construct the fingerprint of a user, which is a serious security and privacy issue. Security of original fingerprints is therefore extremely important. Ali et al. have designed a system for secure fingerprint biometrics; however, their technique has various limitations and is not optimized. In this paper, first we have proposed a secure technique which is highly robust, optimized, and fast. Secondly, unlike most of the fingerprint biometrics apart from the minutiae point location and orientation, we have used the quality of minutiae points as well to construct an optimized template. Third, the template constructed is in 3D shell shape. We have rigorously evaluated the technique on nine different fingerprint databases. The obtained results from the experiments are highly promising and show the effectiveness of the technique.

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“…Однако множество КТ, а также отношений между ними, может быть сохранено в шаблоне различными способами. Наиболее часто для сравнения нескольких пар КТ применяется гребневый счет, разность расстояний между ними и разность их векторов направлений [7][8][9][10]. Под гребневым счетом понимают число линий на изображении, которые пересекаются отрезком, проведенным от одной КТ до другой [3,11].…”

Section: обзор предшествующих работunclassified

Accelerated Fingerprint Identification Method

Гудков¹

2021

CMSE

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В статье излагается метод ускоренной идентификации изображений отпечатков пальцев на основе шаблонов, которые формируются в результате автоматической обработки изображений. Метод опирается на свойства ближайших окрестностей контрольных точек в виде окончаний и разветвлений линий узоров пальцев и состоит из двух этапов. На первом этапе каждая контрольная точка запросного шаблона сравнивается с каждой контрольной точкой ссылочного шаблона из базы данных и оценивается степень похожести таких пар контрольных точек. Для ускорения вычислительных операций вводятся классы, которые позволяют быстро аккумулировать степень похожести контрольных точек из этих двух шаблонов в гистограмме. Оценивается качество такой гистограммы. Гистограммы строятся для всех ссылочных шаблонов из базы данных и одного запросного шаблона. На втором этапе на основе оценок гистограмм отбираются наиболее похожие шаблоны, число которых значительно меньше объема базы данных. Эти шаблоны сравниваются дополнительно с учетом консолидации контрольных точек и оценивается компактность расположения соответствующих пар контрольных точек из двух сравниваемых шаблонов. Значительное ускорение алгоритма идентификации достигается за счет отбрасывания непохожих пар контрольных точек на первом этапе и пар шаблонов с плохими оценками гистограмм на втором этапе. Приводятся результаты экспериментов, опубликованные в интернете.Ключевые слова: отпечаток пальца, идентификация, контрольная точка, гистограмма.

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A local feature vector for an adaptive hybrid fingerprint matcher

Cited by 14 publications

References 13 publications

Survey on the convergence of learning techniques for precise pattern identification in fingerprint analysis

Survey on the convergence of learning techniques for precise pattern identification in fingerprint analysis

Optimized Authentication System with High Security and Privacy

Accelerated Fingerprint Identification Method

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