Biometrics Recognition Using Deep Learning: A Survey

Minaee, Shervin; Abdolrashidi, AmirAli; Su, Hang; Bennamoun, Mohammed; Zhang, David

doi:10.48550/arxiv.1912.00271

Cited by 15 publications

(14 citation statements)

References 172 publications

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“…The convolutional model has been used to improves the quality of the image in low-light frames from very speed video endoscopy and was used to classify the existence of respiratory cysts through Image data, detection of pediatric TB (Tuberculosis) through respiratory X-ray images data, automatic marking of nodules during endoscopy images, cystoscopy of video image analysis. Deep learning strategies [42,43] on chest X-Rays are becoming famous with the development of deep Convolutional network and the results obtained that have been shown in various applications. In addition, a variety of data is required for the training of various learning models.…”

Section: Background Workmentioning

confidence: 99%

Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

Kumar¹,

Pja

2021

AIMS Public Health

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The issue in respiratory sound classification has attained good attention from the clinical scientists and medical researcher's group in the last year to diagnosing COVID-19 disease. To date, various models of Artificial Intelligence (AI) entered into the real-world to detect the COVID-19 disease from human-generated sounds such as voice/speech, cough, and breath. The Convolutional Neural Network (CNN) model is implemented for solving a lot of real-world problems on machines based on Artificial Intelligence (AI). In this context, one dimension (1D) CNN is suggested and implemented to diagnose respiratory diseases of COVID-19 from human respiratory sounds such as a voice, cough, and breath. An augmentation-based mechanism is applied to improve the preprocessing performance of the COVID-19 sounds dataset and to automate COVID-19 disease diagnosis using the 1D convolutional network. Furthermore, a DDAE (Data De-noising Auto Encoder) technique is used to generate deep sound features such as the input function to the 1D CNN instead of adopting the standard input of MFCC (Mel-frequency cepstral coefficient), and it is performed better accuracy and performance than previous models. Results As a result, around 4% accuracy is achieved than traditional MFCC. We have classified COVID-19 sounds, asthma sounds, and regular healthy sounds using a 1D CNN classifier and shown around 90% accuracy to detect the COVID-19 disease from respiratory sounds. Conclusion A Data De-noising Auto Encoder (DDAE) was adopted to extract the acoustic sound signals in-depth features instead of traditional MFCC. The proposed model improves efficiently to classify COVID-19 sounds for detecting COVID-19 positive symptoms.

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Section: Background Workmentioning

confidence: 99%

Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

Kumar¹,

Pja

2021

AIMS Public Health

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“…Human fingerprints are one of the most common biometric attributes used for authentication and identification, from border control identification, through authorizing payments to the daily use of unlocking electronic devices such as cellphones [1], [2]. Current state-of-the-art fingerprint recognition systems are mostly based on deep learning models [3], [4]. While these systems show exceptional performance, they require expensive large-scale fingerprint datasets for training and evaluation.…”

Section: Introductionmentioning

confidence: 99%

Synthesis and Reconstruction of Fingerprints using Generative Adversarial Networks

Bouzaglo¹,

Keller²

2022

Preprint

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Deep learning-based models have been shown to improve the accuracy of fingerprint recognition. While these algorithms show exceptional performance, they require large-scale fingerprint datasets for training and evaluation. In this work, we propose a novel fingerprint synthesis and reconstruction framework based on the StyleGan2 architecture, to address the privacy issues related to the acquisition of such large-scale datasets. We also derive a computational approach to modify the attributes of the generated fingerprint while preserving their identity. This allows synthesizing multiple different fingerprint images per finger. In particular, we introduce the SynFing synthetic fingerprints dataset consisting of 100K image pairs, each pair corresponding to the same identity. The proposed framework was experimentally shown to outperform contemporary state-of-the-art approaches for both fingerprint synthesis and reconstruction. It significantly improved the realism of the generated fingerprints, both visually and in terms of their ability to spoof fingerprint-based verification systems. The code and fingerprints dataset are publicly available: https://github.com/rafaelbou/fingerprint generator.

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“…Deep learning (DL)-based variations, specifically convolutional neural networks (CNNs), have been shown to perform better than timeless AI approaches. Recently, various processing system ideas and clinical image evaluation tasks have been utilized in several issues, including categorization, dissection, and face identification (15)(16)(17)(18)(19).…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Identification System Using Transfer Learning Technique With Mobile-NetV2 and Chest X-Ray Images

Ragab

Alshehri

Azim

et al. 2022

Front. Public Health

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Diagnosis is a crucial precautionary step in research studies of the coronavirus disease, which shows indications similar to those of various pneumonia types. The COVID-19 pandemic has caused a significant outbreak in more than 150 nations and has significantly affected the wellness and lives of many individuals globally. Particularly, discovering the patients infected with COVID-19 early and providing them with treatment is an important way of fighting the pandemic. Radiography and radiology could be the fastest techniques for recognizing infected individuals. Artificial intelligence strategies have the potential to overcome this difficulty. Particularly, transfer learning MobileNetV2 is a convolutional neural network architecture that can perform well on mobile devices. In this study, we used MobileNetV2 with transfer learning and augmentation data techniques as a classifier to recognize the coronavirus disease. Two datasets were used: the first consisted of 309 chest X-ray images (102 with COVID-19 and 207 were normal), and the second consisted of 516 chest X-ray images (102 with COVID-19 and 414 were normal). We assessed the model based on its sensitivity rate, specificity rate, confusion matrix, and F1-measure. Additionally, we present a receiver operating characteristic curve. The numerical simulation reveals that the model accuracy is 95.8% and 100% at dropouts of 0.3 and 0.4, respectively. The model was implemented using Keras and Python programming.

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Biometrics Recognition Using Deep Learning: A Survey

Cited by 15 publications

References 172 publications

Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice

Synthesis and Reconstruction of Fingerprints using Generative Adversarial Networks

COVID-19 Identification System Using Transfer Learning Technique With Mobile-NetV2 and Chest X-Ray Images

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