Fast minutiae extractor using neural network

Zhou, Baicun; Han, Congying; Liu, Yonghong; Guo, Tong; Jin, Qin

doi:10.1016/j.patcog.2020.107273

Cited by 15 publications

(6 citation statements)

References 11 publications

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“…In this study, we remove the attention module from the direction computation branch of ContactlessMinuNet while the other components of the network are retained. In the third ablation study, we replace the loss function of direction regression with that based on the phase angle from a recently published paper [21] .…”

Section: Resultsmentioning

confidence: 99%

“…But it may results in quantization error and the number of categories is large which make it challenging for classification. In the previous study for minutiae extraction [21] , the smaller one of

and

is used to measure the distance between the predicted and ground truth directions, where the direction is normalized to

. This may result in the problem of gradient computation.…”

Section: Proposed Methodsmentioning

confidence: 99%

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A multi-task fully deep convolutional neural network for contactless fingerprint minutiae extraction

Zhang

Liu

2021

Pattern Recognition

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Section: Resultsmentioning

confidence: 99%

and

is used to measure the distance between the predicted and ground truth directions, where the direction is normalized to

. This may result in the problem of gradient computation.…”

Section: Proposed Methodsmentioning

confidence: 99%

A multi-task fully deep convolutional neural network for contactless fingerprint minutiae extraction

Zhang

Liu

2021

Pattern Recognition

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“…For the minutiae extraction, Nguyen et al [16] proposed a universal minutiae extractor based on a modified U-shaped network for segmentation. Zhou et al [17] proposed a network consisting of two stages. In the first stage, a network produces initial candidate patches of minutiae; in the second stage, another network can extract the direction and precise minutia location of each patch.…”

Section: Related Workmentioning

confidence: 99%

An Automatic Deep Neural Network Model for Fingerprint Classification

Mahmoud¹,

Awad²,

Behery³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The accuracy of fingerprint recognition model is extremely important due to its usage in forensic and security fields. Any fingerprint recognition system has particular network architecture whereas many other networks achieve higher accuracy. To solve this problem in a unified model, this paper proposes a model that can automatically specify itself. So, it is called an automatic deep neural network (ADNN). Our algorithm can specify the appropriate architecture of the neural network used and some significant parameters of this network. These parameters are the number of filters, epochs, and iterations. It guarantees the highest accuracy by updating itself until achieving 99% accuracy then it stops and outputs the result. Moreover, this paper proposes an end-to-end methodology for recognizing a person's identity from the input fingerprint image based on a residual convolutional neural network. It is a complete system and is fully automated whether in the features extraction stage or the classification stage. Our goal is to automate this fingerprint recognition system because the more automatic the system is, the more time and effort it saves. Our model also allows users to react by inputting the initial values of these parameters. Then, the model updates itself until it finds the optimal values for the parameters and achieves the best accuracy. Another advantage of our algorithm is that it can recognize people from their thumb and other fingers and its ability to recognize distorted samples. Our algorithm achieved 99.75% accuracy on the public fingerprint dataset (SOCOFing). This is the best accuracy compared with other models.

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“…Recent approaches to minutiae extraction in the literature have found that deep networks are capable of delivering superior minutiae extraction performance in comparison to traditional approaches [39], [40], [41], [42]. Furthermore, the authors in [43] showed that deep learning based minutiae extractors are particularly well suited for low quality fingerprint images such as latent fingerprint images.…”

Section: Minutiae Extractionmentioning

confidence: 99%

Infant-ID: Fingerprints for Global Good

Engelsma¹,

Deb²,

Cao³

et al. 2020

Preprint

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In many of the least developed and developing countries, a multitude of infants continue to suffer and die from vaccine-preventable diseases and malnutrition. Lamentably, the lack of official identification documentation makes it exceedingly difficult to track which infants have been vaccinated and which infants have received nutritional supplements. Answering these questions could prevent this infant suffering and premature death around the world. To that end, we propose Infant-Prints, an end-to-end, low-cost, infant fingerprint recognition system. Infant-Prints is comprised of our (i) custom built, compact, low-cost (85 USD), high-resolution (1,900 ppi), ergonomic fingerprint reader, and (ii) high-resolution infant fingerprint matcher. To evaluate the efficacy of Infant-Prints, we collected a longitudinal infant fingerprint database captured in 4 different sessions over a 12-month time span (December 2018 to January 2020), from 315 infants at the Saran Ashram Hospital, a charitable hospital in Dayalbagh, Agra, India. Our experimental results demonstrate, for the first time, that Infant-Prints can deliver accurate and reliable recognition (over time) of infants enrolled between the ages of 2-3 months, in time for effective delivery of vaccinations, healthcare, and nutritional supplements (TAR=95.

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Fast minutiae extractor using neural network

Cited by 15 publications

References 11 publications

A multi-task fully deep convolutional neural network for contactless fingerprint minutiae extraction

A multi-task fully deep convolutional neural network for contactless fingerprint minutiae extraction

An Automatic Deep Neural Network Model for Fingerprint Classification

Infant-ID: Fingerprints for Global Good

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