Automatic Students Attendance Marking System Using Image Processing And Machine Learning

Patil, Vidya; Narayan, Anushka; Ausekar, Vaishnavi; Dinesh, Anahita

doi:10.1109/icosec49089.2020.9215305

Cited by 12 publications

(8 citation statements)

References 12 publications

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“…Vidya Patil, et al [3] proposed an automatic students attendance marking system using Kth nearest neighbour (KNN). Here, they were acquiring images from the camera, then preprocessing of the image is done by using Histogram equilization method and the face is detected using HAAR cascade algorithm and then features are extracted using the LDA algorithm and then face recognition is done by using three algorithm namely LDA, SVM and KNN .The limitations with system was KNN dont work properly when the dataset is too large and also it is sensitive to noisy and missing data.…”

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%

“…If someone fails to bring his identity card, then he won't be marked present. The digital way of taking attendance is usually carried out with the help of biometric features [3]. Recognizing facial features is one such biometric way to improve digital systems for taking attendance.…”

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

show abstract

“…For the ELM classifier, optimization of weights is required which need to be considered. Patil et al(2020) propose the use of image processing and machine learning for automatic student attendance marking system [8]. Using face recognition under controlled environments the attendance is marked on excel sheet once the face is recognized.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Learning Based Hybrid Approach For Facial Emotion Detection

Sundaramoorthy¹,

Babuvignesh²

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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Humans exhibit an essential set of feelings which might be exhibited via steady facial expressions. Emotions have an important role in teaching as well as other disciplines. It is understood that, while culture, the environment, the language and behavior of people vary, emotions are considered to be universal among the entire human population. Emotions are also important in the realm of education, because their power rivals that of the environment or the language used to communicate information. It's important for cognitive functions including learning and digesting new information.Student emotions during lectures play an important role whether it's in classrooms or in virtual learning environments. Unfortunately, due to the adoption of a virtual method of study or simply the incapacity of humans to keep track of all students in a classroom, teachers do not always keep students' emotions in check, making it difficult to adjust and keep the line of communication open.Thus automated facial emotion detection is very useful and brings the hidden indicators of students internal emotions to limelight. The suggested method aids in the detection of emotions and their classification in order to detect interest in a topic, which is then used as feedback to the course management staff in order to improve learner experience and aid in course material updates.

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“…Patil et al [7] suggested an approach for automatic students attendance marking system using image processing and machine learning. Face detection is done by Viola-Jones algorithm.…”

Section: Literature Surveymentioning

confidence: 99%

Automatic Attendance Monitoring System Using Deep Learning

Shanmuhappriya¹,

G²

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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The manual method of attendance management is time consuming and difficult to maintain. So, the process of management of attendance in schools and universities should be automated. Many biometric systems can be used to record attendance. Face recognition is used frequently. The proposed work aims to record attendance without human interference. In this method the camera is fixed in the classroom.Faces are detected from the captured image and then recognized using trained model. The attendance is then marked and verified by class teacher. The attendanceis marked and absenteeism is sent to the parents. The proposed tooluses Deep Convolution Neural Network(DCNN) in which Max-Margin Object Detection(MMOD) and Histogram of Orientation Gradient(HOG) in Dlib used for face detection. Facenet algorithm responsible for extracting high quality features from the given faces is used for face recognition. The model is trained using K-Nearest Neighbour(KNN) and the framework is loaded in Jetson nano for testing.

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Automatic Students Attendance Marking System Using Image Processing And Machine Learning

Cited by 12 publications

References 12 publications

Face recognition Attendance system using HOG and CNN algorithm

Face recognition Attendance system using HOG and CNN algorithm

Deep Learning Based Hybrid Approach For Facial Emotion Detection

Automatic Attendance Monitoring System Using Deep Learning

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