Student Attendance System using Face Recognition

Dev, Samridhi; Patnaik, Tushar

doi:10.1109/icosec49089.2020.9215441

Cited by 60 publications

(10 citation statements)

References 3 publications

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“…: Sistem Absensi Berbasis Pengenalan Wajah DOI: 10.34148/teknika.v11i1.424 contohnya masker, karena apabila wajah tertutup oleh masker program tidak dapat mendeteksi hidung dan mulut sehingga wajah tidak terdeteksi. Orientasi wajah juga tidak boleh terputar, seperti orientasi wajah terputar atau miring, karena program diatur untuk dapat mendeteksi wajah pada orientasi lurus [12].…”

Section: B Deteksi Wajahunclassified

Sistem Absensi Berbasis Pengenalan Wajah

Miyata

Pranjoto

2022

teknika

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Virus COVID-19 menyebar melalui droplet dari mulut dan hidung yang dapat menyebar melalui sentuhan. Sistem absensi yang biasa digunakan berbasis sentuhan seperti sidik jari maupun Radio-frequency identification (RFID). Untuk mengurangi penyebaran virus COVID-19 diperlukan absensi yang tidak berbasis sentuhan, karena itu dipilih sistem absensi menggunakan wajah. Untuk dapat mengenali wajah dibutuhkan metode guna mendeteksi keberadaan wajah, kemudian mendapatkan data orthoghonal dari wajah. Data orthogonal dari 2 foto orang yang sama akan memiliki kesamaan 60-80%, sedangkan orang yang berbeda akan dibawah 60%. Program mempunyai akurasi 95% saat melihat orang yang terdaftar pada database namun akurasi berkurang saat melihat orang yang tidak terdapat pada database menjadi sebesar 80% saat dilakukan uji coba menggunakan 20 orang yang tidak terdaftar. Lebih susah bagi sistem untuk mengenali bahwa orang tersebut tidak terdaftar karena apabila batas bawah kecocokan ditingkatkan akan berpengaruh dengan kemampuan sistem mendeteksi wajah orang yang terdaftar. Untuk meningkatkan akurasi dapat dilakukan dengan cara memiliki database foto resolusi tinggi, pencahayaan mencerminkan ruangan yang digunakan untuk absensi, dan menggunakan gambar terbaru.

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Section: B Deteksi Wajahunclassified

Sistem Absensi Berbasis Pengenalan Wajah

Miyata

Pranjoto

2022

teknika

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“…Samridhi Dev et al [12] states that the improvement of their system is meant to achieve digitization of the conventional system for taking attendence by calling names and keeping up with pen and paper records. The proposed framework utilizes Haar classifiers, Gabor filters, Generative adversarial networks, SVM (Support Vector Machine), CNN and KNN (k-nearest neighbors).…”

Section: Literature Surveymentioning

confidence: 99%

Supervisory framework for threat detection withmultilayer processing in CNN

Mule¹,

Pandit²,

Karmakar³

et al. 2022

ITM Web Conf.

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Face recognition has received huge acknowledgement due to its various uses in Internet communication, security, access control, surveillance, PC entertainment and law enforcement. Conventional methods of recognition based on the ownerships of identity-cards or full knowledge such as a security number or password are not totally solid. Physical ID cards can be lost or forged, passwords can be hacked or forgotten but a face is undoubtedly connected to its owner. It cannot be stolen, borrowed or easily forged. Our current system has a lot of weaknesses wherever it is simply taken and merged. The focus of this paper is to help users for development of the security by utilizing face identification and recognition. The proposed framework principally comprises of subsystems specifically picture capture, face identification and detection, email alerts and metal detection. Furthermore, the improvement in Computer Vision through Deep Learning algorithms has been an impressive achievement, especially with the Convolutional Neural Network algorithm. A convolutional neural network is a feed-forward neural network that is by and large used to break down visual pictures by using grid-like topology. It is also called as ConvNet. The objects in a picture are distinguished and arranged using convolutional neural network. CNN detects various simple complex patterns in images and data in its different layers of Convolution Layer, Max Polling Layer and Fully Connected Layer .This field intends to allow and configure machines to see the world as people do, and utilize the information for doing tasks (such as Image Analysis, Image Recognition and Classification, etc).

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“…There may be a possibility of getting the image blurred as all the students will be moving. So an enhanced image will be passed to the system for face detection [5,6]. The system will now first detect the faces in the image , then it will extract the facial features, and at the end it will recognize the students names using those features.…”

Section: Introductionmentioning

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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Student Attendance System using Face Recognition

Cited by 60 publications

References 3 publications

Sistem Absensi Berbasis Pengenalan Wajah

Sistem Absensi Berbasis Pengenalan Wajah

Supervisory framework for threat detection withmultilayer processing in CNN

Face recognition Attendance system using HOG and CNN algorithm

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