Impact of digital fingerprint image quality on the fingerprint recognition accuracy

Alsmirat, Mohammad A.; Al-Alem, Fatima Abdalla; Al‐Ayyoub, Mahmoud; Jararweh, Yaser; Gupta, Brij B.

doi:10.1007/s11042-017-5537-5

Cited by 166 publications

(53 citation statements)

References 39 publications

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“…biometric template protection [117], [118] or conventional cryptographic techniques [119], [120]. Similar to face, for other characteristics certain aspects require more in-depth analysis, e.g., biometric quality estimation of (morphed) fingerprint [121], [122] or iris samples [123], [124], respectively. The reported face image morphing attack detection accuracy is yet not reflecting generalization to datasets incorporating the real world variety of capture conditions.…”

Section: Discussionmentioning

confidence: 99%

Face Recognition Systems Under Morphing Attacks: A Survey

et al. 2019

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Recently, researchers found that the intended generalizability of (deep) face recognition systems increases their vulnerability against attacks. In particular, the attacks based on morphed face images pose a severe security risk to face recognition systems. In the last few years, the topic of (face) image morphing and automated morphing attack detection has sparked the interest of several research laboratories working in the field of biometrics and many different approaches have been published. In this paper, a conceptual categorization and metrics for an evaluation of such methods are presented, followed by a comprehensive survey of relevant publications. In addition, technical considerations and tradeoffs of the surveyed methods are discussed along with open issues and challenges in the field. INDEX TERMS Biometrics, face morphing attack, face recognition, image morphing, morphing attack detection.

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Section: Discussionmentioning

confidence: 99%

Face Recognition Systems Under Morphing Attacks: A Survey

et al. 2019

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“…We came across a selection of papers that we cite here as a starting point on the following themes: Measurement of image quality using crowd-based learning [ [129] , [130] , [131] ]. Image quality and its impact on the accuracy of matchers [ 132 ]. Latent print matching using minutiae [ 133 ], sweat pores [ 134 ], pores in conjunction with ridge skeleton [ 135 ], extended minutiae types such as enclosures and crossings [ 136 ], improving on the minutiae matching algorithms [ 137 ], dealing with overlapping marks [ 138 ] or taking advantage of SIFT [ 139 , 140 ] or deep learning techniques [ 141 , 142 ].…”

Section: Friction Ridge Skin and Its Individualization Processmentioning

confidence: 99%

Interpol review of fingermarks and other body impressions 2016–2019

Bécue

Eldridge

Champod

2020

Forensic Science International: Synergy

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“…Recognition and localisation are two critical tasks in intelligent system development [15], [16]. In the area of manufacturing, feature recognition refers to the task for predicting the correct number and type of features appeared in the given CAD model, while feature localisation refers to the task of finding the precise locations of features in the CAD model.…”

Section: Related Workmentioning

confidence: 99%

Intersecting Machining Feature Localization and Recognition via Single Shot Multibox Detector

Shi

Qin

et al. 2021

IEEE Trans. Ind. Inf.

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In Industrie 4.0, machines are expected to become autonomous, self-aware and self-correcting. One important step in the area of manufacturing is feature recognition that aims to detect all the machining features from a 3D model. In this research area, recognising and locating a wide variety of highly intersecting features are extremely challenging as the topology information of features is substantially damaged because of the feature intersection. Motivated by the single shot multibox detector (SSD), this paper presents a novel deep learning approach named SsdNet to tackle the machining feature localisation and recognition problem. The typical SSD is designed for 2D image objection detection rather than 3D feature recognition. Therefore, the network architecture and output of SSD are modified to fulfil the purpose of this research. In addition, some advanced techniques are also utilised to further enhance the recognition performance. Experimental results on the benchmark dataset confirm that the proposed method achieves the state-of-the-art feature recognition performance (95.20% F-score), localisation performance (90.62% F-score) and recognition efficiency (243.85 milliseconds per model).

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Impact of digital fingerprint image quality on the fingerprint recognition accuracy

Cited by 166 publications

References 39 publications

Face Recognition Systems Under Morphing Attacks: A Survey

Face Recognition Systems Under Morphing Attacks: A Survey

Interpol review of fingermarks and other body impressions 2016–2019

Intersecting Machining Feature Localization and Recognition via Single Shot Multibox Detector

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