Automated Attendance System based on Facial Recognition using Adaboost Algorithm

Sharma, Harshil; Mangla, Bhavay; Safa, M.; Saranya, G.; Arun, D

doi:10.1109/icpects56089.2022.10046982

Cited by 1 publication

(1 citation statement)

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“…The proposed method for automatically recording student attendance is based on the development of face recognition techniques. The proposed system for developing face recognition techniques depends on the use of Haar cascade algorithm [25] for faces detection, and then the fusion of both the CNN and the AdaBoost [26] classifiers, as shown in Figure 1. The steps and explanation of implementing the proposed method for face recognition will be discussed.…”

Section: Methods and Implementationmentioning

confidence: 99%

CNN and Adaboost fusion model for multiface recognition based automated verification system of students attendance

Hussain Hassan,

Moussa,

M. Mahmoud

2024

IJEECS

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In recent times, companies and institutions globally are increasingly adopting automated systems for recording employee attendance due to the inefficiency and error-prone nature of traditional methods. Face recognition is the fastest, most natural, and most accurate way to identify someone, despite its difficulty. Remote deployment and control of the technology using internet of things (IoT) protocols provides real-time attendance data worldwide. We use the Haar-cascade algorithm to detect and extract features and the adaptive boost algorithm confused with convolutional neural network (CNN) algorithm to recognize the face in our proposed smart attendance system. Per frame, the proposed system recognizes multiple faces. Face recognition in 18 conditions was designed into the proposed system to ensure its versatility. The system's graphical user interface (GUI) was made for average users. This work is more important because IoT technology records student attendance and sends data to authorities. We use Raspberry Pi 4 and camera module for our suggested system. Python and OpenCV libraries tested the multiple face image recognition proposal in 18 situations under four conditions. Single-face image recognition was compared to other methods. In most cases, the proposed method was 100% accurate and outperformed related methods.

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Section: Methods and Implementationmentioning

confidence: 99%