Research on partial fingerprint recognition algorithm based on deep learning

Fanfeng, Zeng; Hu, Shengda; Xiao, Ke

doi:10.1007/s00521-018-3609-8

Cited by 31 publications

(11 citation statements)

References 19 publications

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“…The performance based on EER was 4.01% and 1.32% respectively. Another partial fingerprint algorithm was proposed by [93] using a DL method based on ResNet. Cross entropy function and contrast-Loss function are the two loss functions used to design the residual network.…”

Section: Application Of Convolutional Neural Network In Fingerprint Image Analysismentioning

confidence: 99%

“…Few studies on GAN demonstrate the efficacy of DL in generating fingerprint images. Different application tasks of the proposed DL-based methods have been identified such as fingerprint classification [15, 67, 79-81, 97, 100, 101, 111, 112], fingerprint liveness detection [65,74,75,77,106,110], fingerprint recognition and authentication [25,26,76], overlapped fingerprint separation [86], double-identity fingerprint detection [87], fingerprint ROI segmentation [88,89], fingerprint alteration detection [94], fingerprint image enhancement [20,96,107,108], latent fingerprint segmentation [83], latent fingerprint recognition [85], latent fingerprint enhancement [84], fingerprint indexing [90,91], fingerprint pore matching [70,99], partial fingerprint matching [92,93], cancelable recognition system [95], fingerprint spoofing detection [6,109], contactless to contact-based and 3D partial fingerprint images matching [104,105], fingerprint minutiae extraction [71,102,103], fingerprint pore extraction [98], fingerprint generation, and presentation attack detection [113,114], fingerprint recovery scheme [115], and fingerprint l...…”

Section: Task For Fingerprint Biometricsmentioning

confidence: 99%

“…Due to the proliferation of mobile devices, there have been mobile fingerprint scanners used to conveniently capture fingerprint. However, the problem with small finger area of mobile fingerprint scanner, inadvertent impressions, low quality of Journal of Artificial Intelligence and Systems ridge impressions, and significant non-linear distortion continue to presents a challenge in partial fingerprint features [93]. The challenge in elastic deformations is inherent on images acquired from the contact-based fingerprint sensors.…”

Section: Open Challenges and Prospect For Future Researchmentioning

confidence: 99%

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Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Chiroma¹

2021

AIS

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Deep Learning algorithms (DL) have been applied in different domains such as computer vision, image detection, robotics and speech processing, in most cases, DL demonstrated better performance than the conventional machine learning algorithms (shallow algorithms). The artificial intelligence research community has leveraged the robustness of the DL because of their ability to process large data size and handle variations in biometric data such as aging or expression problem. Particularly, DL research in automatic fingerprint recognition system (AFRS) is gaining momentum starting from the last decade in the area of fingerprint pre-processing, fingerprints quality enhancement, fingerprint feature extraction, security of fingerprint and performance improvement of AFRS. However, there are limited studies that address the application of DL to model fingerprint biometric for different tasks in the fingerprint recognition process. To bridge this gap, this paper presents a systematic literature review and an insightful meta-data analysis of a decade applications of DL in AFRS. Discussion on proposed model’s tasks, state of the art study, dataset, and training architecture are presented. The Convolutional Neural Networks models were the most saturated models in developing fingerprint biometrics authentication. The study revealed different roles of the DL in training architecture of the models: feature extractor, classifier and end-to-end learning. The review highlights open research challenges and present new perspective for solving the challenges in the future. The author believed that this paper will guide researchers in propose novel fingerprint authentication scheme.

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Section: Application Of Convolutional Neural Network In Fingerprint Image Analysismentioning

confidence: 99%

Section: Task For Fingerprint Biometricsmentioning

confidence: 99%

Section: Open Challenges and Prospect For Future Researchmentioning

confidence: 99%

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Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Chiroma¹

2021

AIS

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“…There are three biometric patterns dwelling in a finger, fingerprint(FP), finger-vein(FV) and finger-knuckle-print(FKP). With convenient collection and relatively compact distribution of trimodal sources of a finger, finger-based multimodal biometric fusion recognition has more universal and practical advantages [7], [8]. Because of the mismatch of finger trimodal feature spaces, to explore a The associate editor coordinating the review of this manuscript and approving it for publication was Zhanyu Ma. reliable and effective finger multimodal features expression and fusion recognition approach, however, has still been a challenging problem in practice [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Graph Fusion for Finger Multimodal Biometrics

Zhang

Shi

et al. 2019

IEEE Access

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In terms of biometrics, a human finger itself is with trimodal traits including fingerprint, finger-vein, and finger-knuckle-print, which provides convenience and practicality for finger trimodal fusion recognition. The scale inconsistency of finger trimodal images is an important reason affecting effective fusion. It is therefore very important to developing a theory of giving a unified expression of finger trimodal features. In this paper, a graph-based feature extraction method for finger biometric images is proposed. The feature expression based on graph structure can well solve the problem of feature space mismatch for the finger three modalities. We provide two fusion frameworks to integrate the finger trimodal graph features together, the serial fusion and coding fusion. The research results can not only promote the advancement of finger multimodal biometrics technology but also provide a scientific solution framework for other multimodal feature fusion problems. The experimental results show that the proposed graph fusion recognition approach obtains a better and more effective recognition performance in finger biometrics.

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“…Li et al [19] present a new method named density-distance and heuristic for identifying temporal protein complexes. Xiao et al [20] proposed a partial fingerprint recognition algorithm based on deep learning for the recognition of partial fingerprint images. It can improve the structure of convolutional neural networks, use two kinds of loss functions for network training and feature extraction, and finally improve the recognition performance of partial fingerprint images.…”

mentioning

confidence: 99%

Special issue on emergence in human-like intelligence toward cyber-physical systems

Yen

2019

Neural Comput & Applic

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As the famous slogan ''Connecting People'' indicates, a lot of the developments in novel technologies intensify the relationship between people without necessarily enhancing technologies that are close to the nature of human beings. Examples can be easily found in recent computing paradigms, such as Cloud Computing that advances network infrastructure for data storage and resource sharing, or the Internet of Things that investigates the intelligence and awareness of objects involved in the network. From this point of view, the existing state-of-the-art solutions, in realms of artificial intelligence and/or computational intelligence, significantly differ from the human competence level in many research domains. Even though it is generally not clear whether human-like approach would show its upper-hand over existing methods, the exploration of this research path seems to be advantageous and challenging. Human-like intelligence is arguably one of the most powerful approaches for the optimization and design of complex Web, as well as its correlated issues so as to meet the requirements and growing needs of new challenging applications (and services as well). Although the state of the art behind the computational intelligence has already reached an impressive scale, there remain quite a lot of attractive research topics, with consideration of human-like or human-oriented factors, for further and promising developments.The submitted manuscripts were reviewed by experts from both academia and industry. After two rounds of reviewing, the highest quality manuscripts were accepted for this special issue. This special issue will be published by Neural Computing and Applications as special issues.

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Research on partial fingerprint recognition algorithm based on deep learning

Cited by 31 publications

References 19 publications

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Graph Fusion for Finger Multimodal Biometrics

Special issue on emergence in human-like intelligence toward cyber-physical systems

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