A Novel Hybrid Biometric Electronic Voting System: Integrating Finger Print and Face Recognition

Syed, Shahram Najam; Shaikh, Aamir Zeb; Naqvi, Shabbar

doi:10.22581/muet1982.1801.05

Cited by 11 publications

(7 citation statements)

References 1 publication

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“…2 by reduced metabolism in the brains of Alzheimer's patients in the temporomandibular area and above the skull, as detected by PET-FDG4. [30][31][32][33] The term "biomarker" refers to any biological marker that may be measured and utilized as an indicator of the presence or absence of a condition, such as a disease [34]. The levels of beta-amyloid and proteins called tau in the CSF uid are one of the rst indicators used to identify Alzheimer's disease [35].…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematical Model and Artificial Intelligence for Diagnosis of Alzheimer's Disease

Davodabadi

Daneshian

Saati

et al. 2023

Preprint

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Degeneration of the neurological system linked to cognitive deficits, daily living exercise clutters, and behavioral disturbing impacts may define Alzheimer's disease. Ad research conducted later in life focuses on describing ways for early detection of dementia, a kind of mental disorder. To tailor our care to each patient, we utilized visual cues to determine how they were feeling. We did this by outlining two approaches to diagnosing a person's mental health. Support vector machine is the first technique (SVM). Image characteristics are extracted using a fractal model for classification in this method. With this technique, the histogram of a picture is modeled after a Gaussian distribution. Classification was performed with several SVM kernels, and the outcomes were compared. Step two proposes using a deep convolutional neural network (DCNN) architecture to identify Alzheimer's-related mental disorders. According to the findings, the SVM approach accurately recognized over 93% of the photos tested. The DCNN approach was one hundred percent accurate during model training, whereas the SVM approach achieved just 93 percent accuracy. In contrast to SVM's accuracy of 89.3%, the DCNN model test's findings were accurate 98.8% of the time. Based on the findings reported here, the proposed DCNN architecture may be used for diagnostic purposes involving the patient's mental state.

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Section: Literature Reviewmentioning

confidence: 99%

Mathematical Model and Artificial Intelligence for Diagnosis of Alzheimer's Disease

Davodabadi

Daneshian

Saati

et al. 2023

Preprint

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“…Biometrics Security: Due to BioHashing function, HB * =h(Bia⊕u), the user's biometrics is secure. Before, entering to the authentication process, the user's put his/her thumb on the sensor, the raw data extracted will pass from an image processing system, from where the important feature will extract and BioHashing function will convert it to a fixed length hash code [20]. The whole scenario is shown in Fig.…”

Section: Denning-sacco Attackmentioning

confidence: 99%

SIP Issues and Challenges – A Scalable Three Factor Authentication Scheme

Jan

Qayum

Khan

2020

Mehran Univ. res. j. eng. technol.

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The SIP (Session Initiation Protocol) is an application and presentation layer signaling protocol used for initiating, continuing and terminating multimedia session for the end user. It gains much attention of the researchers because it is exposed to several threats and noticed challenging vulnerabilities from time to time. Consequently, the security of SIP is a crucial task and many efforts have been made by different researchers and tried to divert the attention towards its solution. But still, no one claims with conviction about a foolproof secure mechanism for SIP. As users extensively use SIP services, the mutual authentication and key agreement among the participants is an important issue. So, robust authentication and key agreement scheme are mandatory for enhancing security, legitimacy and better complexities. Therefore, we present an improved three-factor authentication scheme that caters all the weakness and known attacks in Mishra et al. scheme. The proposed scheme not only guarantees for security but performance can also be made lightweight. As performance and security contradict each other, the change in one inversely affects the other. The proposed scheme has been analyzed both formally using BAN (Burrows-Abadi-Needham) logic and ProVerif1.93 software verification toolkit, and informally using assumptions which show a delicate balance of security with performance.

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“…In [2], multiple trait verification techniques were developed using fingerprint and facial recognition. The system had an accuracy of 91% for facial recognition and 98% for fingerprint recognition.…”

Section: Basic Concepts and Related Workmentioning

confidence: 99%

“…This research used fingerprint biometric authentication to enhance the security, resilience of [5]. It also reduces the response time in [12], while maintaining an accuracy of 94% compared to the 91% achieved by [2].…”

Section: Basic Concepts and Related Workmentioning

confidence: 99%

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Development of A Fingerprint Biometric Authentication System For Secure Electronic Voting Machines

Umar

Olaniyi

Ajao

et al. 2019

KINETIK

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Democratic government in the world today rely on electronic voting as the foremost means of providing credible, transparent and fair elections for the electorate. There is a need for developed electronic voting systems to be security enhanced to ensure the authenticity of the developed system. Traditional paper balloting systems suffer from vote tampering, multiple voting and illegal voting by unregistered voters. They are also, susceptible to under aged voting due to the difficulty in authenticating the identity of prospective voters. Manual collation and publication of vote results also leads to slow response times and inaccuracies in published results. This research paper proposes a system to combat the current challenges through the development of a fingerprint biometric authentication system for secure electronic voting machines. It uses a fingerprint biometric sensor, integrated via Python to verify users of the system. The inclusion of biometrics improves the security features of the system. The secure voting system is built using PHP and easy to use Graphical User Interface was designed using HTML and CSS. Users are required to interact with the machine via a 7” touchscreen interface. From the results, it shows that the developed machine has a minimum response time of 0.6 seconds for specific operation, an FAR of 2%, FRR of 10% and overall system accuracy of 94%. The developed machine is able to combat the challenges of authentication of users, thereby guaranteeing the transparency, credibility, integrity and vote authenticity of the elections.

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A Novel Hybrid Biometric Electronic Voting System: Integrating Finger Print and Face Recognition

Cited by 11 publications

References 1 publication

Mathematical Model and Artificial Intelligence for Diagnosis of Alzheimer's Disease

Mathematical Model and Artificial Intelligence for Diagnosis of Alzheimer's Disease

SIP Issues and Challenges – A Scalable Three Factor Authentication Scheme

Development of A Fingerprint Biometric Authentication System For Secure Electronic Voting Machines

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