An android based course attendance system using face recognition

Sunaryono, Dwi; Siswantoro, Joko; Anggoro, Radityo

doi:10.1016/j.jksuci.2019.01.006

Cited by 64 publications

(40 citation statements)

References 8 publications

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“…Machine learning is an artificial intelligence that is able to learn the character of data (Sunaryono, 2019). In this study, machine learning is used to study data with images.…”

Section: Methodsmentioning

confidence: 99%

The Tomatoes and Chilies Type Classifications by Using Machine Learning Methods

Sabilla

Fatichah

2020

JDR

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Vegetables are ingredients for flavoring, such as tomatoes and chilies. A Both of these ingredients are processed to accompany the people's staple food in the form of sauce and seasoning. In supermarkets, these vegetables can be found easily, but many people do not understand how to choose the type and quality of chilies and tomatoes. This study discusses the classification of types of cayenne, curly, green, red chilies, and tomatoes with good and bad conditions using machine learning and contrast enhancement techniques. The machine learning methods used are Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), and Random Forest (RF). The results of testing the best method are measured based on the value of accuracy. In addition to the accuracy of this study, it also measures the speed of computation so that the methods used are efficient.

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“…Machine learning is an artificial intelligence that is able to learn the character of data (Sunaryono, 2019). In this study, machine learning is used to study data with images.…”

Section: Methodsmentioning

confidence: 99%

The Tomatoes and Chilies Type Classifications by Using Machine Learning Methods

Sabilla

Fatichah

2020

JDR

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“…A conventional approach to record student attendance is performed by asking every student to sign on an attendance list that passes through all students during the beginning of lectures. With the growing number of Android smartphone, Sunaryono et al [12], Khan et al [13], Elias et al [14], and Shen et al [15] have developed a mobile-based attendance system using face recognition based approach. Face recognition can also be applied for computer security purpose by using an available face databases.…”

Section: Face Recognition As An Enablersmentioning

confidence: 99%

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

Moung¹

2020

IJATCSE

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In the past decade, face recognition has gained an important role among the most frequently used image processing applications and the availability of viable technologies in this field has also contributed significantly to this. Face recognition has become an enabler in healthcare, surveillance, photo cataloging, attendance, and much more, which will be discussed in this review paper. Despite rapid progress in face-recognition technology, various challenges such as variations, occlusion, facial expressions, aging and many more that affect the performance of the system still need to be addressed. This paper presents a review on the state-of-the-art, enablers, challenges and solutions for face recognition. Face recognition can be categorized into three groups; namely global approach, local feature approach, and hybrid approach. The global approach uses the whole face as input for the face recognition system. The local approach uses measurements between important landmarks of a face and certain face region selection for training. The hybrid approach blends global and local approaches in which the hybrid approach uses the best global and local approach methods. The challenges of face recognition are; (i) automated face detection where difficulties lies on detecting a person's face, (ii) pose variations cause by rotation of people's head, (iii) face occlusion, (iv) facial expression changes, (v) ageing of the face, (vi) varying illumination conditions, (vii) low image resolution, (viii) identity look-alike or twin, and (ix) other technical difficulties. Finally, the solutions to each of the highlighted challenges were described. The survey found that all the images considered for training and testing were made up of RGB images. With the rapid growth of computer technology in terms of computing speed and the increasingly sophisticated functions of smartphones, multispectral or even hyperspectral imagery could be considered for face-recognition research.

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“…Face recognition requires a camera placed at the entrance [7], front of the classroom [8][9][10], or using smartphone camera [11], it is also possible to combine other media to narrow the scope of face recognition so that it only compares with certain users [12][13]. In [13] student can record their attendace using their smartphone by scanning the QR Code on the monitor in front of the class and capturing their face, this solution only works if student connected in a university Wi-Fi network.…”

Section: Related Workmentioning

confidence: 99%

Student Attendance System Using Wifi Direct and Temporary Wi-Fi Hotspot

Pambudi

Bariyah

2020

Jurnal Ilmu Komputer dan Informasi

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Manual attendance recording throws away a lot of teaching and administration time from the university. Research on automatic attendance recording that has been done can be divided into biometrics and non-biometrics uses. Almost all methods require additional device that it is costly and inflexible for class changes. The proposed method solves the problems by utilizing the standard features of smartphones that are owned by all student, this method uses Wi-Fi direct for class broadcasting process and temporary Wi-Fi hotspot for verification process. The experimental results show that the proposed method produces the time needed for the initialization process is 14980 ms and the verification process is 3640 ms.

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An android based course attendance system using face recognition

Cited by 64 publications

References 8 publications

The Tomatoes and Chilies Type Classifications by Using Machine Learning Methods

The Tomatoes and Chilies Type Classifications by Using Machine Learning Methods

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

Student Attendance System Using Wifi Direct and Temporary Wi-Fi Hotspot

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