Low‐quality fingerprint classification using deep neural network

Tertychnyi, Pavlo; Özçinar, Çağrı; Anbarjafari, Gholamreza

doi:10.1049/iet-bmt.2018.5074

Cited by 33 publications

(16 citation statements)

References 36 publications

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“…Most current approaches extract minutiae from fingerprint images and perform fingerprint matching based on the number of corresponding minutiae pairings [3,8,33,35]. Shell et al [33],questions regarding human fingerprint orientation and found that expertise in fingerprints increases the accuracy of marked-up orientation field, which is a characteristic feature.…”

Section: Related Workmentioning

confidence: 99%

“…Although fingerprints theoretically can identify people with high accuracy, the real-world performance of the systems highly depends on the condition of the finger's surface, i.e., humidity, dust, temperature, etc., which can drop the identification accuracy [35]. Features such as OF, which is representing the trend of the ridge flow of fingerprint [16,42], are usually used for low-quality fingerprint segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…However, this task is usually either computationally expensive or is time-consuming [20,44]. Tertychnyi et al [35] have proposed a ConvNets based technique which can identify the type of low-quality fingerprints. However, the challenge of improving the quality of fingerprints is still open.…”

Section: Introductionmentioning

confidence: 99%

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Cycle-consistent generative adversarial neural networks based low quality fingerprint enhancement

Karabulut

Tertychnyi

Arslan

et al. 2020

Multimed Tools Appl

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Distortions such as dryness, wetness, blurriness, physical damages and presence of dots in fingerprints are a detriment to a good analysis of them. Even though fingerprint image enhancement is possible through physical solutions such as removing excess grace on the fingerprint or recapturing the fingerprint after some time, these solutions are usually not user-friendly and time consuming. In some cases, the enhancements may not be possible if the cause of the distortion is permanent. In this paper, we are proposing an unpaired image-to-image translation using cycle-consistent adversarial networks for translating images from distorted domain to undistorted domain, namely, dry to not-dry, wet to not-wet, dotted to not-dotted, damaged to not-damaged, blurred to not-blurred. We use a database of low quality fingerprint images containing 11541 samples with dryness, wetness, blurriness, damages and dotted distortions. The database has been prepared by real data from VISA application centres and have been provided for this research by GEYCE Biometrics. For the evaluation of the proposed enhancement technique, we use VGG16 based convolutional neural network to assess the percentage of enhanced fingerprint images which are labelled correctly as undistorted. The proposed quality enhancement technique has achieved the maximum quality improvement for wetness fingerprints in which 94% of the enhanced wet fingerprints were detected as undistorted.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cycle-consistent generative adversarial neural networks based low quality fingerprint enhancement

Karabulut

Tertychnyi

Arslan

et al. 2020

Multimed Tools Appl

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“…Moreover, this recognition approach is widely recognized for its authentication precision and the likelihood that the same fingerprint occurring in two persons is minimal. All said and done, there are other instances where the system fails to recognize some fingerprints due to the possibility that the fingerprint reader may lose its sensitivity with time, or a user's fingerprint might be damaged [8]. ese problems lend us to two main errors in recognition, thus Type-I and Type-II errors.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition

Appati

Nartey

Owusu

et al. 2021

International Journal of Mathematics and Mathematical Sciences

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Biometrics consists of scientific methods of using a person’s unique physiological or behavioral traits for electronic identification and verification. The traits for biometric identification are fingerprint, voice, face, and palm print recognition. However, this study considers fingerprint recognition for in-person identification since they are distinctive, reliable, and relatively easy to acquire. Despite the many works done, the problem of accuracy still persists which perhaps can be attributed to the varying characteristic of the acquisition devices. This study seeks to improve the issue recognition accuracy with the proposal of the fusion of a two transform and minutiae models. In this study, a transform-minutiae fusion-based model for fingerprint recognition is proposed. The first transform technique, thus wave atom transform, was used for data smoothing while the second transform, thus wavelet, was used for feature extraction. These features were added to the minutiae features for person recognition. Evaluating the proposed design on the FVC 2002 dataset showed a relatively better performance compared to existing methods with an accuracy measure of 100% as to 96.67% and 98.55% of the existing methods.

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“…Deep neural networks, due to their high accuracy, are widely used in many of the computer vision applications such as emotion recognition [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ], biometric recognition [ 17 , 18 , 19 , 20 ], personality analysis [ 21 , 22 ], and activity analysis [ 5 , 23 , 24 ]. Depending on the nature of the data, different structures can be used [ 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Action Recognition Using Single-Pixel Time-of-Flight Detection

Ofodile

Helmi

Clapés

et al. 2019

Entropy

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Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject’s privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene. Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47 % accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network.

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Low‐quality fingerprint classification using deep neural network

Cited by 33 publications

References 36 publications

Cycle-consistent generative adversarial neural networks based low quality fingerprint enhancement

Cycle-consistent generative adversarial neural networks based low quality fingerprint enhancement

Implementation of a Transform-Minutiae Fusion-Based Model for Fingerprint Recognition

Action Recognition Using Single-Pixel Time-of-Flight Detection

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