Biometric Template Protection Scheme-Cancelable Biometrics

Gunjan, Vinit Kumar; Prasad, Puja S.; Mukherjee, Saurabh

doi:10.1007/978-981-13-8715-9_48

Cited by 18 publications

(9 citation statements)

References 9 publications

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“…When asked to discriminate between parasitized and uninfected cells in an image dataset of 27,558 cell photos, Shaik et al [ 16 ] proposed a customized, sequential CNN with three convolutional layers and two fully connected layers, which they found to be effective. Using pretrained CNNs, such as AlexNet, VGG-16 [ 17 ], Xception [ 18 ], ResNet-50 [ 19 ], and DenseNet-121 [ 20 ], the authors evaluated the effectiveness of the CNNs in extracting attributes from parasitized and uninfected cells. For the AlexNet and VGG-16 models, features were extracted from the second fully connected layer, while for the Xception, ResNet-50, and DenseNet-121 models, features were extracted from the last layer before the final classification layer.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning and Transfer Learning for Malaria Detection

Jameela

Athotha

Singh

et al. 2022

Computational Intelligence and Neuroscience

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Infectious disease malaria is a devastating infectious disease that claims the lives of more than 500,000 people worldwide every year. Most of these deaths occur as a result of a delayed or incorrect diagnosis. At the moment, the manual microscope is considered to be the most effective equipment for diagnosing malaria. It is, on the other hand, time-consuming and prone to human error. Because it is such a serious global health issue, it is important that the evaluation process be automated. The objective of this article is to advocate for the automation of the diagnosis process in order to eliminate the need for human intervention in the process. Convolutional neural networks (CNNs) and other deep-learning technologies, such as image processing, are being utilized to evaluate parasitemia in microscopic blood slides in order to enhance diagnostic accuracy. The approach is based on the intensity characteristics of Plasmodium parasites and erythrocytes, which are both known to be variable. Images of infected and noninfected erythrocytes are gathered and fed into the CNN models ResNet50, ResNet34, VGG-16, and VGG-19, which are all trained on the same dataset. The techniques of transfer learning and fine-tuning are employed, and the outcomes are contrasted. The VGG-19 model obtained the best overall performance given the parameters and dataset that were evaluated.

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Section: Related Workmentioning

confidence: 99%

Deep Learning and Transfer Learning for Malaria Detection

Jameela

Athotha

Singh

et al. 2022

Computational Intelligence and Neuroscience

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“…Cancelable fingerprint templates are designed using non-invertible transformations performed on the original fingerprint data [18]. Some of the common cancelable biometric templates and its advantages are given in [5,55,57] and [67].…”

Section: Related Workmentioning

confidence: 99%

A novel fingerprint template protection and fingerprint authentication scheme using visual secret sharing and super-resolution

Muhammed

Mhala

Pais

2020

Multimed Tools Appl

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Fingerprint is the most recommended and extensively practicing biometric trait for personal authentication. Most of the fingerprint authentication systems trust minutiae as the characteristic for authentication. These characteristics are preserved as fingerprint templates in the database. However, it is observed that the databases are not secure and can be negotiated. Recent studies reveal that, if a person's minutiae points are dripped, fingerprint can be restored from these points. Similarly, if the fingerprint records are lost, it is a permanent damage. There is no mechanism to replace the fingerprint as it is part of the human body. Hence there is a necessity to secure the fingerprint template in the database. In this paper, we introduce a novel fingerprint template protection and fingerprint authentication scheme using visual secret sharing and super-resolution. During enrollment, a secret fingerprint image is encrypted into n shares. Each share is stored in a distinct database. During authentication, the shares are collected from various databases. The original secret fingerprint image is restored using a multiple image super-resolution procedure. The experimental results show that the reconstructed fingerprints are similar to the original fingerprints. The proposed method is robust, secure, and efficient in terms of fingerprint template protection and authentication.

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“…Biometric is a physical and biological quality of an individual which is different for every person [6][7]. There are different types of biometric traits among which are facial recognition, Fingerprint, iris recognition, and palm print [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A secured automated bimodal biometric electronic voting system

Okokpujie

John

et al. 2021

IJ-AI

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Insecurity, rigging and violence continue to mar electoral processes in developing nations. It has been difficult to enforce security and transparency in the voting process. This paper proposes a secure and automated bimodal voting system. The system uses three security layers, namely, a unique ID code, a token passcode that expires every five minutes and biometrics (iris and fingerprint). A scanner captures the fingerprint and iris of eligible voters. The fingerprint and iris images stored along with the corresponding particulars in a database. The software implemented is a .net managed code in C#. The result of this system shows the system is transparent, fast and fraud-free. The proposed method had a failure to enroll (FTE) and a failure to capture (FTC) of zero.

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Biometric Template Protection Scheme-Cancelable Biometrics

Cited by 18 publications

References 9 publications

Deep Learning and Transfer Learning for Malaria Detection

Deep Learning and Transfer Learning for Malaria Detection

A novel fingerprint template protection and fingerprint authentication scheme using visual secret sharing and super-resolution

A secured automated bimodal biometric electronic voting system

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