A Web-Based Mobile Attendance System with Facial Recognition Feature

The main target of the work in this paper is to detect the gender and oldness of a person with an accurate decision and efficient time based on the number of facial outward attributes extracted using Linear-Discriminate Analysis to classify a person within a certain category according to his(her) gender and age. This work was deal with color facial images via the Iterative Dichotomiser3 algorithm as a classifier to detect the oldness of a person after gender detected. This paper used the Face-Gesture-Recognition-Research-Network aging dataset. All facial images in the dataset were categorizing into binary categories using k-means. This is followed by the process of dividing all samples according to age classes that belonging to each specific sex category. Thus, this division process enabled us to reach a quick and accurate decision. The results showed that the accuracy of the proposal was 90.93%, and F-measure was 89.4.

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“…and Root-Mean-Square-Error R.M.S.E. are measures of error rate in prediction [29,30]. Nevertheless, R.M.S.E.…”

Section: Gain S a Entropy S S S Entropysmentioning

confidence: 99%

Human Gender and Age Detection Based on Attributes of Face

Shaker

Al-Khalidi

2022

Int. J. Interact. Mob. Technol.

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“…This paper identifies faces through the camera. The procedure for recognizing faces consists of two phases: The first is the process of detecting human faces in an image or video (which is a series of successive images), which involves extracting the human face (or faces) present in the image and differentiating it from the image's background, such as the sky, clouds, buildings, fields, or even differentiating it from the rest of the human body since the human face is the region of interest (RoI) in the process of recognizing faces [5]. In the second step, a person's face is recognized by comparing it with another face in the database.…”

Section: Introductionmentioning

confidence: 99%

Facial Recognition Technology-Based Attendance Management System Application in Smart Classroom

Lateef,

Kamil

2023

ijcsm

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Traditional attendance recording methods can be replaced with artificial intelligence (AI) methods. This paper aims to develop an automated attendance system with a user-friendly graphical interface in Arabic to enhance user interaction. The Python programming language is used to build the system, which employs object detection and feature extraction algorithms. This system processes a live broadcast from an IP camera installed in the classroom. The system employs object detection algorithms to detect the faces in the video. The face locations are then sent to the feature extraction algorithms to extract and compare features with the features stored in the database. Our paper concludes that automating the attendance registration process using facial recognition technology can achieve up to 100% accuracy. Comparing our proposed system with previous studies, we find that it outperforms them in several aspects. Our method handles real-time tests with a larger number of students. It accommodates students positioned at different distances from the camera within the classroom, demonstrating our method's effectiveness in automating the attendance registration process.

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“…Some problems arise when educators face online meetings. One of them is the difficulty of monitoring or supervising students when the teacher explains [4]. The main problem is that students must focus on listening to the presented material.…”

Section: Introductionmentioning

confidence: 99%

Smart System to Recapitulate Student Attendance on Virtual Meeting Platforms During Covid-19

Rahmad

Syulistyo

Putra

et al. 2023

Int. J. Interact. Mob. Technol.

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Educators have problems conducting online learning, such as monitoring student attendance while presenting the material. This paper aims to predict student names who attend zoom video conferences with various lighting conditions and face angles by comparing two detection and two recognition methods. This paper proposes an intelligent system based on the use of a bot that will analyse a combination of face detection and recognition method for attendance systems using video conferencing applications to carry out online learning. The proposed system will use the best combination of two methods to recapitulate student attendance. The face detection system uses Haar Cascade and MTCNN, and the face recognition system uses ResNet and FaceNet. The tests were conducted on video zoom footage taken during online lectures. The results show that MTCNN and FaceNet get the highest accuracy, 93.23%.

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A Web-Based Mobile Attendance System with Facial Recognition Feature

Cited by 6 publications

References 4 publications

Human Gender and Age Detection Based on Attributes of Face

Human Gender and Age Detection Based on Attributes of Face

Facial Recognition Technology-Based Attendance Management System Application in Smart Classroom

Smart System to Recapitulate Student Attendance on Virtual Meeting Platforms During Covid-19

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