Automated Attendance Marking and Management System by Facial Recognition Using Histogram

S, Jenif D Souza W; Jothi, S. Arul; Chandrasekar, A.

doi:10.1109/icaccs.2019.8728399

Cited by 23 publications

(2 citation statements)

References 4 publications

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“…For this reason, there is a recent interest in the automation of attendance taking. The authors in [7]- [17] used Haar cascade classifier to automatically detect faces of subjects in captured images, after which the captured face images are compared with face images in a database. Once a match is found, the attendance record of the subject is automatically updated.…”

Section: Introductionmentioning

confidence: 99%

Development of mobile and desktop applications for a fingerprint-based attendance management system

Akinola

Olopade

Afolabi

2021

IJEECS

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Mobile application technology has been at the forefront of technological advancement in recent years. This has made life easier, and tasks that were considered herculean have been made easier and executable in a much shorter time than ever. One of such tasks is the process of taking attendance during events (such as lectures and conferences) by scribbling one’s signature and other personal details on a central register. This manual process is cumbersome and inconvenient, especially when a large number of participants are involved. To address this problem, this paper presents an automated solution in which a Java-based mobile application was developed and connected wirelessly to a central database that was created using My structured query language (MySQL) application whose task, among others, was to record attendance information. The database was connected to the backend of the web-based software program which was coded in hypertext pre-processor (PHP) programming language. Authentication was achieved through username, password, and fingerprint information. The system was deployedin a university to log students’ details, time absent, time present and cumulative attendances per month and it was realised that the system was highly effective, efficient and 5 times faster than the conventional paper-based attendance logging technique.

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Section: Introductionmentioning

confidence: 99%

Development of mobile and desktop applications for a fingerprint-based attendance management system

Akinola

Olopade

Afolabi

2021

IJEECS

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“…Souza et al[52] developed an emergency management system based on face recognition. After capturing the image, the face is segmented and the face is detected using the Haar-like feature algorithm, and a validation is done in the database.…”

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confidence: 99%

Machine learning models in people detection and identification

2022

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Introduction: This article is the result of research entitled "Development of a prototype to optimize access conditions to the SENA-Pescadero using artificial intelligence and open-source tools", developed at the Servicio Nacional de Aprendizaje in 2020. Problem: How to identify Machine Learning Techniques applied to computer vision processes through a literature review? Objective: Determine the application, as well as advantages and disadvantages of machine learning techniques focused on the detection and identification of people. Methodology: Systematic literature review in 4 high-impact bibliographic and scientific databases, using search filters and information selection criteria. Results: Machine Learning techniques defined as Principal Component Analysis, Weak Label Regularized Local Coordinate Coding, Support Vector Machines, Haar Cascade Classifiers and EigenFaces and FisherFaces, as well as their applicability in detection and identification processes. Conclusion: The research led to the identification of the main computational intelligence techniques based on machine learning, applied to the detection and identification of people. Their influence was shown in several application cases, but most of them were focused on the implementation and optimization of access control systems, or tasks in which the identification of people was required for the execution of processes. Originality: Through this research, we studied and defined the main machine learning techniques currently used for the detection and identification of people. Limitations: The systematic review is limited to information available in the 4 databases consulted, and the amount of information is variable as articles are deposited in the databases.

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Authentication and Access Control by Face Recognition and Intrusion Detection System

Das,

Roychowdhury

et al. 2023

Communications in Computer and Information Science

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Automated Attendance Marking and Management System by Facial Recognition Using Histogram

Cited by 23 publications

References 4 publications

Development of mobile and desktop applications for a fingerprint-based attendance management system

Development of mobile and desktop applications for a fingerprint-based attendance management system

Machine learning models in people detection and identification

Authentication and Access Control by Face Recognition and Intrusion Detection System

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