Study of Implementing Automated Attendance System Using Face Recognition Technique

Kar, Nirmalya; Debbarma, Mrinal Kanti; Saha, Ashim; Pal, Dwijen Rudra

doi:10.7763/ijcce.2012.v1.28

Cited by 117 publications

(30 citation statements)

References 10 publications

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“…The Hyke system is described as a lowcost alternative to the more expensive solutions, intended to be used mainly in the developing world regions. Kar et al [40] describes a method for tracking of student attendance to classes that works using face recognition technology, implementing the OpenCV library. The system provides automatic attendance recording with zero wasted time, but, as the authors report, the system's accuracy in detecting and recognizing faces in 95 % for frontal face orientation which drops to zero when the angle of the face toward the camera is 72 degrees.…”

Section: A Review Of Related Attendance Tracking Systemsmentioning

confidence: 99%

On the Technologies and Systems for Student Attendance Tracking

Kotevski¹,

Blazheska-Tabakovska²,

Bocevska³

et al. 2018

IJITCS

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Manual student attendance tracking, by calling student names from a check list or taking students' signs on a paper, has gone into history. Nowadays, modern technologies have already enabled the development of various automatized attendance tracking systems. These technologies include: Radio-Frequency Identification (RFID), Biometric (fingerprint, face or voice recognition), Barcode identification and Bluetooth communication technologies, that are implemented over an IP infrastructure as a platform. But, all these technologies perform in a different manner and exhibit certain functional limitations considering the given implementation. The main motivation for this research was to explore the possibilities for overcoming the issues of current systems for attendance tracking, considering the limitations of the technologies employed. Hence, the core contribution of this research can be considered as a fourfold, i.e. i) it presents the most frequently used technologies in the development of attendance tracking systems, ii) it reviews a range of existing student attendance tracking systems, iii) it defines criteria for performance evaluation of the technologies employed in student attendance tracking, from a perspective of educational institutions, and iv) it evaluates the mostly used technologies according to the predefined functional criteria. As a summary of the evaluation it provides directions for future development of a student attendance tracking system that would address the explored issues and functional limitations.

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Section: A Review Of Related Attendance Tracking Systemsmentioning

confidence: 99%

On the Technologies and Systems for Student Attendance Tracking

Kotevski¹,

Blazheska-Tabakovska²,

Bocevska³

et al. 2018

IJITCS

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“…Principal Component Analysis (PCA) adalah salah satu metode berbasis penampilan yang popular digunakan [3] untuk mereduksi dimensi dari sekumpulan atau ruang citra sehingga basis atau koordinat yang baru dapat menggambarkan model yang khas dari kumpulan tersebut [10]. Identifikasi biometrik dengan mengimplementasikan metode haar cascade dan algoritma PCA yang dilakukan [6] berhasil mengintegrasi pendeteksian dan pengenalan wajah ke dalam sistem kehadiran.…”

Section: Algoritma Pca (Principal Component Analysis)unclassified

Rancang Bangun Aplikasi Pengenalan Wajah menggunakan Metode Viola-Jones dan Algoritma PCA

Yuliana

Nurhaida

2018

InComTech

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Sistem identifikasi persensi berkembang dengan cepat. Perkembangan tersebut, mendorong kemajuan sistem keamanan berbasis biometrik. Pengenalan wajah merupakan salah satu sistem identifikasi persensi yang dikembangkan berdasarkan perbedaan ciri wajah dari seseorang berbasis biometrik dengan memiliki keakuratan tinggi. Metode viola-jones digunakan untuk proses pendeteksian yang merupakan hasil dari gabungan fitur-fitur khusus, sedangkan eigenface merupakan algoritma berdasarkan Principal Component Analysis (PCA) yang mudah diimplementasikan dan dimulai dengan pemrosesan awal untuk menemukan hasil citra yang lebih baik, setelah itu menghitung eigenvector dan eigenvalue dari citra wajah yang ditrainingkan. Aplikasi pengenalan wajah menampilkan data dari citra wajah yang telah tersimpan dalam database. Pengujian menggunakan berbagai ekspresi sebanyak 200 citra dari 20 orang. Persentasi keberhasilan proses pengenalan wajah adalah 95%. Beberapa faktor yang mempengaruhi keberhasilan pengenalan yaitu pencahayaan, jarak wajah serta posisi wajah.

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“…Eigen face, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Gabor Wavelets, Elastic Bunch Graph Matching, 3D morphable Model and Hidden Markov Models. We will try to show some of them in this paper [1].…”

Section: Introductionmentioning

confidence: 99%

“…Authentication is an important issue in system control in computer based communication. Human face recognition is a significant branch of biometric verification and has been used widely in many applications, such as video monitor system, human-computer interaction, and door control system and network security [1]. In this paper we are concerned with objects motion so we try to modify face recognition technique to recognize faces in a video stream using Local Binary Pattern histogram with processed data.…”

Section: Introductionmentioning

confidence: 99%

An Accurate System for Face Detection and Recognition

Ali

El‐Hafeez

Mohany

2019

JAMCS

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During the last few years, Local Binary Patterns (LBP) has aroused increasing interest in image processing and computer vision. LBP was originally proposed for texture analysis, and has proved a simple yet powerful approach to describe local structures. It has been extensively exploited in many applications, for instance, face image analysis, image and video retrieval, environment modeling, visual inspection, motion analysis, biomedical and aerial image analysis, remote sensing. Face recognition is an interesting and challenging problem, and impacts important applications in many areas such as identification for law enforcement, authentication for banking and security system access, and personal identification among others. In this paper we are concerned with face recognition in a video stream using Local Binary Pattern histogram with processed data. First we will detect faces by using a combination of Haar cascade files that uses skin detection, eye detection and nose detection as input of LBP to increase the accuracy of the proposed recognition system. Also, our system can be used to build a dataset of faces and names to be used in a recognition step. The experimental results have shown that the proposed system can achieve accuracy of recognition up to 96.5% which was better than the relevant methods.

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Study of Implementing Automated Attendance System Using Face Recognition Technique

Cited by 117 publications

References 10 publications

On the Technologies and Systems for Student Attendance Tracking

On the Technologies and Systems for Student Attendance Tracking

Rancang Bangun Aplikasi Pengenalan Wajah menggunakan Metode Viola-Jones dan Algoritma PCA

An Accurate System for Face Detection and Recognition

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