Automatic attendance management system using face detection

Varadharajan, E.; Dharani, R.; Jeevitha, S.; Kavinmathi, B.; Hemalatha, S.

doi:10.1109/get.2016.7916753

Cited by 48 publications

(17 citation statements)

References 2 publications

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“…These methods do not require any installation in the system. The attendance can be done by just capturing an image through a mobile application [13], using the camera to automatically capture the image of the students and then mark the attendance for them [14], or capture the picture and uploaded to the system [15]. Real-time face recognition usually reduces the accuracy of the recognition due to the movement of the head, noise interfering and light condition changing [11].…”

Section: Attendance Methodsmentioning

confidence: 99%

Automatic Attendance System Using Face Recognition with Deep Learning Algorithm

Al-Amoudi

Samad

Abdullah

et al. 2021

Lecture Notes in Electrical Engineering

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This project aims to develop an attendance system that is more efficient and convenient than traditional attendance methods currently used in schools and universities. Therefore, this paper proposes an automatic attendance system using face recognition. In this face recognition attendance system, the university does not need to install any additional devices in the classroom, which makes it a cost-effective system. The system consists of three parts: attendance system, student profile system, and training. First is the training stage where the student's photo should be captured and stored in a separate folder. Second is the attendance system. Here the lecturer needs to take a photograph of the student and then upload it to the system. The system will automatically recognize the student's face and store his/her name in an excel sheet (CVS file). The third system is the student's profile. This system is to help the lecturer retrieve the student's data by only capturing a picture of the student. A GUI has been made to simplify the usage of the system. The face recognition system has been developed using a combination of two deep learning algorithms: Multi-Task Cascaded Convolutional Neural Network (MTCNN) and FaceNet. To train the system, 908 pictures from 21 different students were collected and used, and 108 pictures were used for testing. The testing result showed 100% for face detection and 87.03% for face recognition.

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Section: Attendance Methodsmentioning

confidence: 99%

Automatic Attendance System Using Face Recognition with Deep Learning Algorithm

Al-Amoudi

Samad

Abdullah

et al. 2021

Lecture Notes in Electrical Engineering

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“…Several tests were performed with different scenarios and the average face recognition rate achieved was 90%. Varadhrajan et al also designed a face recognition system to take attendance [20]. The faces in the image are detected and cropped separately.…”

Section: Literature Reviewmentioning

confidence: 99%

Is Face Recognition with Masks Possible?

Saib¹,

Pudaruth²

2021

IJACSA

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With the recent outbreak of the COVID-19 pandemic, wearing face masks has become extremely important to protect us, and to reduce the spread of the virus. This measure has made many existing face recognition systems ineffective as they were trained to work with unmasked faces. In this paper, several methods have been proposed for masked face recognition. Two pre-trained deep learning architectures (VGG16, and MobileNetV2) and the Histogram of Gradients (HOG) technique were used to extract the relevant features from face images of celebrities. A SoftMax layer and Support Vector Machines (SVM) were used for classification. Five scenarios were devised to assess the different models and approaches. With an accuracy of 96.8%, the best model was obtained with MobileNetV2 with a SoftMax layer on the dataset consisting of a mixture of masked and unmasked images. Three different types of masks were also used in this study. The mean accuracy was 91.35% when the same type of mask is used for training and testing. However, the accuracy dropped by an average of 5.6% when a different type of mask is used for training and testing. A contactless attendance system using the best masked face recognition model has also been implemented.

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“…The only limitation of this system is that it requires a high computational time and thus the implementation cost increases. In paper [6] the authors use PCA along with MATLAB to implement a face recognition system. The result from this system was that the pre-processing of the image and cropping the region of interest from it made the accuracy of the system higher.…”

Section: Literature Surveymentioning

confidence: 99%

Online Attendance Marking System Using Facial Recognition and Intranet Connectivity

Pant

Sharma

Annapurani

et al. 2021

Recent Trends in Intensive Computing

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Keeping up the attendance record with everyday exercises is a difficult task. The conventional method of marking staff attendance is by tapping their ID card and then using fingerprint scanner. But due to COVID-19 pandemic the attendance system of using fingerprint scanner is stalled and currently not in use. The following system depends on face recognition and intranet connectivity to keep up attendance record of facilities and staff. The paper discusses the attendance marking system that is passive (no direct contact with the scanner or sensor) and restricting the users within certain network. The main goal of this system is divided in two steps, in initial step face is snare from the front camera of the smart phone and it is then recognized in the picture and in the second step these distinguished appearances and features are contrasted with stored information in data set for confirmation.

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Automatic attendance management system using face detection

Cited by 48 publications

References 2 publications

Automatic Attendance System Using Face Recognition with Deep Learning Algorithm

Automatic Attendance System Using Face Recognition with Deep Learning Algorithm

Is Face Recognition with Masks Possible?

Online Attendance Marking System Using Facial Recognition and Intranet Connectivity

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