Attendance System for Face Recognition using GSM module

Hubballi, Kirti B; Bhat, Vandana S.

doi:10.26438/ijcse/v6i7.11801184

Cited by 2 publications

(2 citation statements)

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“…The system was also tested on a real-time attendance scenario with 30 enrolled students and achieved an accuracy rate of 93.3%. B. Kavinmathi et al [4] proposed an automated system based on CNNs. Here the GSM module is used to send the generated attendance report.…”

Section: Literature Surveymentioning

confidence: 99%

Attendance System by Face Recognition Using Deep Learning

2024

IRJMETS

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In colleges, universities, organizations, schools, and offices, taking attendance is one of the most important tasks that must be done on a daily basis. The majority of the time, it is done manually, such as by calling by name or by roll number. The main goal of this project is to create a Face Recognition-based attendance system that will turn this manual process into an automated one. This project meets the requirements for bringing modernization to the way attendance is handled, as well as the criteria for time management. This device is installed in the classroom, where and student's information, such as name, roll number, class, sec, and photographs, is trained. The images are extracted using Open CV. Before the start of the corresponding class, the student can approach the machine, which will begin taking pictures and comparing them to the qualified dataset. Logitech C270 web camera and NVIDIA Jetson Nano Developer kit were used in this project as the camera and processing board. The image is processed as follows: first, faces are identified using a Haarcascade classifier, then faces are recognized using the LBPH (Local Binary Pattern Histogram) Algorithm, histogram data is checked against an established dataset, and the device automatically labels attendance. An Excel sheet is developed, and it is updated every hour with the information from the respective class instructor. Due to the fact that CNNs achieve the best results for larger datasets, which is not the case in production environment, the main challenge was applying these methods on smaller datasets. A new approach for image augmentation for face recognition tasks is proposed. The overall accuracy was 95.02% on a small dataset of the original face images of employees in the real-time environment. The proposed face recognition model could be integrated in another system with or without some minor alternations as a supporting or a main component for monitoring purposes.

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Section: Literature Surveymentioning

confidence: 99%

Attendance System by Face Recognition Using Deep Learning

2024

IRJMETS

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“…So we will take each frame from the video obtained by the cameras and try to find faces in it. If we found face then we will go ahead for centralization of face else we will go for the next frame of the video [7]- [8]. This process of image acquisition is shown in figure 2.…”

Section: System Designmentioning

confidence: 99%

Face recognition Attendance system using HOG and CNN algorithm

Kapse¹,

Kamble²,

Lohar³

et al. 2022

ITM Web Conf.

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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.

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Attendance System for Face Recognition using GSM module

Cited by 2 publications

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Attendance System by Face Recognition Using Deep Learning

Attendance System by Face Recognition Using Deep Learning

Face recognition Attendance system using HOG and CNN algorithm

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