Face detection and recognition in real-time photos with haar cascade and local binary pattern histogram for automatic door locking system

2021 ◽  
Author(s):  
Ian Haikal A ◽  
Raihan P. ◽  
Wiwit Rahayu ◽  
Dea Febrianty ◽  
Nailil Farihah ◽  
...  
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Face Detection And Recognition ◽  
Haar Cascade ◽  
Pattern Histogram ◽  
Detection And Recognition

scholarly journals Real time Face Detection and Optimal Face Mapping for Online Classes

2022 ◽  
Vol 2161 (1) ◽  
pp. 012063
Author(s):  
MCP Archana ◽  
CK Nitish ◽  
Sandhya Harikumar
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Online Classes ◽  
Web Based ◽  
Online Classrooms ◽  
Face Detection And Recognition ◽  
Haar Cascade ◽  
Improved Accuracy

Abstract The main objective of this paper is to provide a web-based tool for identifying faces in a real-time environment, such as Online Classes. Face recognition in real-time is now a fascinating field with an ever-increasing challenge such as light variations, occlusion, variation in facial expressions, etc. During the current pandemic scenario of COVID-19, the demand for online classrooms has rapidly increased. This has escalated the need for a real-time, economic, simple, and convenient way to track the attendance of the students in a live classroom. This paper addresses the aforementioned issue by proposing a real-time online attendance system. Two alternative face recognition algorithms are perceived in order to develop the tool for realtime face detection and recognition with improved accuracy. The algorithms adopted are Local Binary Pattern Histogram(LBPH) and Convolutional Neural Network (CNN) for face recognition as well as Haar cascade classifier with boosting for face detection. Experimental results show that CNN with an accuracy of 95% is better in this context than LBPH that yields an accuracy of 78%.

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