Design and Implementation of a Student Attendance System Using Iris Biometric Recognition

Okokpujie, Kennedy; Noma‐Osaghae, Etinosa; Okesola, Olatunji; John, Samuel; Okonigene, R. E.

doi:10.1109/csci.2017.96

Cited by 44 publications

(28 citation statements)

References 3 publications

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“…Where, N, in this instance, is the sums of iteration,f_i is the training loss values and y_i is the testing loss values. Consequently, MSE is calculated, as presented in (2) [36], [37].…”

Section: Mean Squared Errormentioning

confidence: 99%

Comparative analysis of augmented datasets performances of age invariant face recognition models

et al. 2021

Self Cite

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The popularity of face recognition systems has increased due to their non-invasive method of image acquisition, thus boasting the widespread applications. Face ageing is one major factor that influences the performance of face recognition algorithms. In this study, the authors present a comparative study of the two most accepted and experimented face ageing datasets (FG-Net and morph II). These datasets were used to simulate age invariant face recognition (AIFR) models. Four types of noises were added to the two face ageing datasets at the preprocessing stage. The addition of noise at the preprocessing stage served as a data augmentation technique that increased the number of sample images available for deep convolutional neural network (DCNN) experimentation, improved the proposed AIFR model and the trait aging features extraction process. The proposed AIFR models are developed with the pre-trained Inception-ResNet-v2 deep convolutional neural network architecture. On testing and comparing the models, the results revealed that FG-Net is more efficient over Morph with an accuracy of 0.15%, loss function of 71%, mean square error (MSE) of 39% and mean absolute error (MAE) of -0.63%.

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“…Where, N, in this instance, is the sums of iteration,f_i is the training loss values and y_i is the testing loss values. Consequently, MSE is calculated, as presented in (2) [36], [37].…”

Section: Mean Squared Errormentioning

confidence: 99%

Comparative analysis of augmented datasets performances of age invariant face recognition models

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…This is because control may not be total especially in risk management processes such as change management where process failure is possible. Detective control is therefore required to identify errors, irregularities or attacks after their occurrences [10,29]. Botnet detection can be classified by botmasters, bots or C&C servers.…”

Section: Detectionmentioning

confidence: 99%

“…How a botnet work [7] Prior to 2013, four primary methods commonly being used to mitigate DDoS attacks were commercial security software, criminal enforcement, botnet seizure by federal agencies, and private civil action [8]. However, such efforts, though valuable, are passive and limited by their focus on prevention [9,10]. A leading cloud based service provider, CloudFlare, therefore offered DDoS protection capable of matching sophisticated DDoS attacks.…”

Section: Introductionmentioning

confidence: 99%

Internet service providers responsibilities in botnet mitigation: a Nigerian perspective

Okesola

Adebiyi

Osi-Okeke

et al. 2020

IJECE

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Botnet-based attack is dangerous and extremely difficult to overcome as all the primary mitigation methods are passive and limited in focus. A combine efforts of Internet Service Providers (ISPs) are better guides since they can monitor the traffic that traverse through their networks. However, ISPs are not legally banded to this role and may not view security as a primary concern. Towards understudying the involvement of ISPs in Botnet mitigation in Nigeria, this study elicited and summarized mitigation measures from scientific literatures to create a reference model which was validated by structured interview. Although, ISPs role is seen to be voluntary and poorly incentivized, the providers still take customers security very serious but concentrate more on preventive and notification measures.

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“…A democratic nation is the people's country. Democratic government can only be right when there is provision for a trustworthy and secured electoral process [1]. E-voting is an emerging technology that has improved the traditional method of voting [2].…”

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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Design and Implementation of a Student Attendance System Using Iris Biometric Recognition

Cited by 44 publications

References 3 publications

Comparative analysis of augmented datasets performances of age invariant face recognition models

Comparative analysis of augmented datasets performances of age invariant face recognition models

Internet service providers responsibilities in botnet mitigation: a Nigerian perspective

A secured automated bimodal biometric electronic voting system

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