Partial Fingerprint Matching via Phase-Only Correlation and Deep Convolutional Neural Network

Jin, Qin; Tang, Siqi; Han, Congying; Guo, Tong

doi:10.1007/978-3-319-70136-3_64

Cited by 8 publications

(22 citation statements)

References 11 publications

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“…The proposed method was evaluated using five databases from NIST and FVC2000 databases and the results produce the best performance in terms of accuracy and efficiency. A partial fingerprint matching algorithm was proposed by Journal of Artificial Intelligence and Systems [92] based on phase-only correlation (POC)-based method and deep CNN. The method first aligned two fingerprints and the features are extracted with the algorithm based on POC.…”

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%

“…For instance, [15] proposed a fingerprint classification using Deep CNN classifier method which was fed with texture features extracted using histogram equalization, Gabor enhancement and fingerprint thinning. A study by [92] proposed a method for partial fingerprint matching. The method starts by aligning two fingerprints in a coarsely fashion using an algorithm based on phase-only correlation.…”

Section: Modelling Deep Learning Architecturementioning

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: Modelling Deep Learning Architecturementioning

confidence: 99%

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Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Chiroma¹

2021

AIS

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“…Finally, a fusion of both scores is calculated. In another work, first an alignment of the partial fingerprints based on phase-only correlation and polar Fourier transform is made (Qin et al, 2017). Then, a deep network is proposed for extracting a fixed-length feature vector from each overlapped region.…”

Section: Good Quality Partial Fingerprint Matchingmentioning

confidence: 99%

Latent Fingerprint Matching Using Distinctive Ridge Points

Castillo-Rosado¹,

Hernández-Palancar²

2019

Informatica

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The way that forensic examiners compare fingerprints highly differs from the behaviour of current automatic fingerprint identification algorithms. Experts usually use all the information in the fingerprint, not only minutiae, while automatic algorithms don't. Partial (especially latent) fingerprint matching algorithms still report low accuracy values in comparison to those achieved by experts. This difference is mainly due to the features used in each case. In this work, a novel approach for matching partial fingerprints is presented. We introduce a new fingerprint feature, named Distinctive Ridge Point (DRP), combined with an improved triangle-based representation which also uses minutiae. The new feature describes the neighbouring ridges of minutiae in a novel way. A modified version of a fingerprint matching algorithm presented in a previous work is used for matching two triangular representations of minutiae and DRPs. The experiments conducted on NIST27 database with a background added of 29000 tenprint impressions from NIST14 and NIST4 databases showed the benefits of this approach. The results show that using the proposal we achieved an accuracy of 70.9% in rank-1, improving in an 11% the accuracy obtained using minutiae and the reference point. This result is comparable with the best accuracy reached in the state of the art while the amount of features is reduced.

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“…Wang et al [14] achieved an outstanding effect in fingerprint classification tasks based on the depth neural network method. In [15], deep learning technique also shows good performance in partial fingerprint matching. Labati et al [16] proposed a two-step CNN method to extract the coordinates of the sweat pores from fingerprint images, which perform first the detection of candidate regions and Then, the extraction of the points of interest in the second step.…”

Section: Introductionmentioning

confidence: 99%

A Method for Singular Points Detection Based on Faster-RCNN

et al. 2018

Self Cite

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Most methods for singular points detection usually depend on the orientation fields of fingerprints, which cannot achieve reliable and accurate detection of poor quality fingerprints. In this study, a new method for fingerprint singular points detection based on Faster-RCNN (Faster Region-based Convolutional Network method) is proposed, which is a two-step process, and an orientation constraint is added in Faster-RCNN to obtain orientation information of singular points. Besides, we designed a convolutional neural network (ConvNet) for singular points detection according to the characteristics of fingerprint images and the existing works. Specifically, the proposed method could extract singular points directly from raw fingerprint images without traditional preprocessing. Experimental results demonstrate the effectiveness of the proposed method. In comparison with other detection algorithms, our method achieves 96.03% detection rate for core points and 98.33% detection rate for delta points on FVC2002 DB1 dataset while 90.75% for core points and 94.87% on NIST SD4 dataset, which outperform other algorithms.

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Partial Fingerprint Matching via Phase-Only Correlation and Deep Convolutional Neural Network

Cited by 8 publications

References 11 publications

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Latent Fingerprint Matching Using Distinctive Ridge Points

A Method for Singular Points Detection Based on Faster-RCNN

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