Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system

Abstract

Face recognition is gaining popularity as one of the biometrics methods for an attendance system in an organization. Due to the pandemic, the common face recognition system needs to be modified to meet the current needs, whereby facemask detection is necessary. The main objective of this paper is to investigate and develop a real-time face recognition system for the attendance system based on the current scenarios. The proposed framework consists of face detection, mask detection, face recognition, and attendance report generation modules. The face and facemask detection is performed using the haar cascade classifier. Two techniques for face recognition were investigated, the eigenfaces and local binary pattern histogram. The initial experimental results and implementation at Kuching Community College show the effectiveness of the system. For future work, an approach that is able to perform masked face recognition will be investigated.

An Attendance System Using the Face Extraction and Recognition Technique Using PCA Algorithm

“…Other authors studied real-time face recognition approaches for attendance systems, namely eigenfaces and local binary pattern histograms (Suhaimin et al, 2021). This research achieved an accuracy rate of 73.3% based on eigenfaces.…”

Face Identification System and Temperature Detection as A Control to Enter the Building

“…Among several biometric techniques, facial recognition is the best scenario to prevent the spread of the Covid-19 virus as well as an attendance system. The research entitled Realtime Mask Detection and Face Recognition using Eigenfaces and Local Binary Pattern Histogram for Attendance System [8] succeeded in making a facial recognition system that was applied as a presence at Kuching Community College face without the need to remove the face mask. Facial recognition can also be used as a web service through chatbots.…”

Sophisticated face mask dataset: a novel dataset for effective coronavirus disease surveillance

“…Hassan et al [9] employed a Jetson Nano, infrared temperature sensor, AMG8833, and C920e camera to achieve 99% and 100% accuracy during training and testing [10] introduced a portable IoT device for COVID-19 guideline enforcement, encompassing mask detection, social distance alerting, crowd analysis, health screening, and assessment. A real-time face recognition system for attendance with mask detection was proposed in [11], investigating eigenfaces and local binary pattern histograms. Mobilenet-V2-based models demonstrated 95% accuracy and a 0.96 F1 score [12], [13] utilized YOLOv3 trained on celebi and wider-face databases to achieve 93.9% accuracy for mask detection on face 1031 detection data set and benchmark (FDDB) [14].…”