Face recognition Attendance system using HOG and CNN algorithm

Abstract

Recognition of faces is one of the most useful applications and has a critical role in the technological field. Recognizing the face is a lively concern for authentication, specifically in the context of taking attendance. Attendance system using face recognition is a process of recognizing the profile of the person by using facial features supported by various computing technology and monitoring. The evolution of this process is focused on achieving the digitizing of the orthodox system of taking manual attendance. Current approaches for taking attendance are monotonous and inefficient. Manual records of attendance can be easily manipulated. The orthodox process of checking attendance such as current fingerprint or card scanning systems are susceptible to proxies. To tackle these issues, this paper has been proposed. The proposed system makes the utilization of various algorithms such as histogram of oriented Gradient (HOG), convolutional neural network (CNN) and support vector machine (SVM). After the face is recognized, the reports of attendance are going to be created, maintained and stored in excel format. The system is examined in various situations like illumination, head movements, and the variation of distance between the face and cameras. The proposed system was found to be efficient and reliable for marking attendance during a classroom with negligible time consumption and no manual work. This system is inexpensive as less installation is required.

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

“…The basic system for recording students' absence through a combination of the use of facial recognition technologies and the IoT begins with extracting images and taking a snapshot, then extracting the facial area from the rest of the image, extracting the distinctive features of the image, comparing the image to the database. If the photo is recognized, the student's attendance will be recorded, an email will be sent to Whom It May Concern, and the task will be The most widely used technique to detect the facial region is the histogram of oriented gradients (HOG) technique [18], and the landmark technique [19] is one of the techniques widely used to extract the distinctive features of each person's facial region. Finally, machine learning [20] and deep learning [21] techniques such as support vector machine (SVM) [22], K-nearest neighbour (KNN) [23], and convolutional neural network (CNN) [24] are used to recognize faces.…”