University Classroom Attendance System Using FaceNet and Support Vector Machine

Nyein, Thida; Oo, Aung Nway

doi:10.1109/aitc.2019.8921316

Cited by 18 publications

(8 citation statements)

References 6 publications

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“…Thida Nyein, et al [1] proposed a Face recognition attendance system using Facenet and support vector machine this system was divided into three parts first one was preprocessing of raw data in which face alignment is done on the raw data then that data is converted in training dataset in which the images are trained with model and classifier where they used facenet for feature extraction and support vector machine for classification. The flow of the ITM Web of Conferences 44, 03028 (2022) https://doi.org/10.1051/itmconf/20224403028 ICACC-2022 proposed system is machine starts from taking image as an input and then face detection is done by using opencv and then feature extraction and embedding is done by using Facenet and feature matching is done by using support vector machine by matching with trained dataset.…”

Section: Literature Surveymentioning

confidence: 99%

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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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Section: Literature Surveymentioning

confidence: 99%

“…Attendance is very important for administration purposes, but usually it can become a tedious activity, with lots of inaccuracies [1]. The orthodox procedures for maintaining attendance have many limitations because it can be extremely difficult to take roll calls and maintain records when the total count of students is high [2].…”

Section: Introductionmentioning

confidence: 99%

Face recognition Attendance system using HOG and CNN algorithm

Kapse¹,

Kamble²,

Lohar³

et al. 2022

ITM Web Conf.

View full text Add to dashboard Cite

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“…Thida Nyein et al (2018), the principle goal of the proposed device [16] is to get a higher accuracy for multi-face reputation with the aid of combining FaceNet and Support Vector Machine. In this proposed device, FaceNet is used for characteristic extraction with the aid of using embedding 128 dimensions in step with face points.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Algorithm for Face Recognition System Based Smart Attendance System

Sathya¹,

Priya²,

Dhanalakshmi³

2022

ECS Trans.

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Face recognition aims to detect, track, identify, or verify person from video or images using recognition system. Its application varies from security, authentication, social media, and so on. With the advancement of technologies, still it impedes in accuracy. The challenges involved in face recognition are variations in facial expression involve diverging light, facial changes interfered with facial hair, pose, and so on. This research work presents a new hybrid approach, which improves the accuracy in identification of a person. The accuracy of the proposed work clearly depicts that it can be best suited in real time environments for tracking persons. It can be used for applications like authentication, security, and so on. The proposed work is validated by tracking the student presence. It captures the image of the student and detect the face by pre-processing, extracting the features and recognizing the person. For recognition it uses Local Binary Pattern Histogram (LBPH) algorithm and Haar Cascade classifier. LBP Algorithm uses Histogram, which improves the accuracy of the face recognition. It is integrated with Haar classifier, which uses machine learning algorithm. Adaboost Learning algorithm, which selects small features from large set, which improves the efficiency of recognition.

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“…Detecting multiple faces from a single frame and poor resolution is challenging. The proposed method in [13] combined Support Vector Machine (SVM) and FaceNet to enhance accuracy of extracted features. Features are extracted by embedding 128 dimensions per face using FaceNet and SVM to classify extracted features with training data.…”

Section: Literature Surveymentioning

confidence: 99%

Security Monitoring System Using FaceNet For Wireless Sensor Network

Preetha¹,

Sheela²

2021

Preprint

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Face Recognition techniques have been rising rapidly over past few decades and gaining attraction in many security systems. Several methodologies are proposed to provide excellent results in face detection. Sensing technology enables several applications such as transportation, healthcare and security. Video surveillance systems trace and identify moving objects. Wireless Sensor networks are used to monitor remote areas and can be applied to monitor a facility by considering each camera as sensor nodes. Cameras are used as nodes in a wireless sensor network with a central server or a gateway node for all the monitoring and analysis of the information retrieved from the nodes. Identification and authentication of users in any organization is quite difficult due to high movement. Face recognition can be used detect faces and identify them continuously in a video feed which can be deployed to continuously monitor an area. Feeding from camera to base station uses Multi-task Cascaded Convolutional Neural Networks (MCTNN) and FaceNet algorithms for face recognition. Further information about the person is sent to all the end-user nodes present in the wireless network. This approach has been implemented and evaluated on a prototype wired camera network called FaceNet to monitor a network and provide security. A method for tracking people in 2D world coordinates and acquiring canonical frontal face images that fits the sensor network paradigm. The approach evaluates and demonstrates the tasking algorithm in action on data acquired from the FaceNet camera network.

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University Classroom Attendance System Using FaceNet and Support Vector Machine

Cited by 18 publications

References 6 publications

Face recognition Attendance system using HOG and CNN algorithm

Face recognition Attendance system using HOG and CNN algorithm

A Hybrid Algorithm for Face Recognition System Based Smart Attendance System

Security Monitoring System Using FaceNet For Wireless Sensor Network

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