scholarly journals Real-Time Attendance and Feedback System

2021 ◽  
pp. 1135-1139
Author(s):  
Piyush Manish Sonar ◽  
Aniket Nitin Chaudhari ◽  
Mehul Deepak Sethi ◽  
Tejaswini Sanjay Gadakh
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Feedback System ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Student Attendance ◽  
Feedback Form ◽  
Face Recognition System ◽  
User Friendly ◽  
Facial Images

Face is the representation of one’s identity. Hence, we have proposed an automated student attendance system based on face recognition. Face recognition system is very useful in life applications especially for attendance system. In our proposed approach, firstly, video framing is performed by activating the camera through a user-friendly interface. In the pre-processing stage, scaling of the size of images is performed, if necessary, in order to prevent loss of information. In face recognition stage, enhanced local binary pattern (LBP) and principal component analysis (PCA) is applied correspondingly in order to extract the features from facial images. Another way of marking the attendance is fingerprint recognition. To mark the attendance students simply have to give the fingerprint impression in fingerprint scanner module. Finally, the attendance of the recognized student will be marked and saved in the excel file. The student who is not registered will also be able to register on the spot and notification will be given if students sign in more than once. Whenever seminar is completed then a link is sent on email. It includes the information in terms of feedback. When student fills the feedback form then analysis of overall session is done.

Development of Class Attendance System Using Face Recognition for Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia

2020 ◽  
pp. 1-42
Author(s):  
Pauline Ong ◽  
Tze Wei Chong ◽  
Woon Kiow Lee
Keyword(s):  
Face Recognition ◽  
Traditional Approach ◽  
Principal Component ◽  
Recognition System ◽  
Support Vector ◽  
Original Image ◽  
Student Attendance ◽  
Biometric Verification ◽  
Face Recognition System ◽  
The Face

The traditional approach of student attendance monitoring system in Universiti Tun Hussein Onn Malaysia is slow and disruptive. As a solution, biometric verification based on face recognition for student attendance monitoring was presented. The face recognition system consisted of five main stages. Firstly, face images under various conditions were acquired. Next, face detection was performed using the Viola Jones algorithm to detect the face in the original image. The original image was minimized and transformed into grayscale for faster computation. Histogram techniques of oriented gradients was applied to extract the features from the grayscale images, followed by the principal component analysis (PCA) in dimension reduction stage. Face recognition, the last stage of the entire system, using support vector machine (SVM) as classifier. The development of a graphical user interface for student attendance monitoring was also involved. The highest face recognition accuracy of 62% was achieved. The obtained results are less promising which warrants further analysis and improvement.

scholarly journals Identification and Recognition of facial images

2020 ◽  
pp. 69-76
Author(s):  
Mallika Kohli ◽  
Vasundra Wazir ◽  
Parul Sharma ◽  
Pawanesh Abrol
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Main Task ◽  
Face Recognition System ◽  
The Face ◽  
Almost All ◽  
Facial Images

Face detection is the power to identify a face and recognition is the ability to recognize whose face it is by means of facial characteristics. Face is multivariate and requires a lot of mathematical summation. Almost all imperative applications use a face recognition system. There are many methods that have been already proposed which provides low recognition rate. Hence, the main task of research is to develop a face recognition system with higher recognition capability and better accuracy. This paper proposes Face recognition system by combining two techniques Viola Jones and Principal Component Analysis. An approach of Eigen faces is employed in Principle Component Analysis(PCA). The face recognition system is implemented in MATLAB.

scholarly journals Real-time Triplet Loss Embedding Face Recognition for Authentication Student Attendance Records System Framework

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Hady Pranoto ◽  
Oktaria Kusumawardani
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Success Factors ◽  
Recognition System ◽  
Expression Recognition ◽  
Student Attendance ◽  
Face Recognition System ◽  
Triplet Loss ◽  
Education Process

The number of times students attend lectures has been identified as one of many success factors in the learning process in many studies. We proposed a framework of the student attendance system by using face recognition as authentication. Triplet loss embedding in FaceNet is suitable for face recognition systems because the architecture has high accuracy, quite lightweight, and easy to implement in the real-time face recognition system. In our research, triplet loss embedding shows good performance in terms of the ability to recognize faces. It can also be used for real-time face recognition for the authentication process in the attendance recording system that uses RFID. In our study, the performance for face recognition using k-NN and SVM classification methods achieved results of 96.2 +/- 0.1% and 95.2 +/- 0.1% accordingly. Attendance recording systems using face recognition as an authentication process will increase student attendance in lectures. The system should be difficult to be faked; the system will validate the user or student using RFID cards using facial biometric marks. Finally, students will always be present in lectures, which in turn will improve the quality of the existing education process. The outcome can be changed in the future by using a high-resolution camera. A face recognition system with facial expression recognition can be added to improve the authentication process. For better results, users are required to perform an expression instructed by face recognition using a database and the YOLO process.

scholarly journals Real-Time Internet Based Attendance Using Face Recognition System

2018 ◽  
Vol 7 (3.15) ◽  
pp. 174 ◽  
Author(s):  
Yuslinda Wati Mohamad Yusof ◽  
Muhammad Asyraf Mohd Nasir ◽  
Kama Azura Othman ◽  
Saiful Izwan Suliman ◽  
Shahrani Shahbudin ◽  
...  
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Recognition System ◽  
Ease Of Use ◽  
Cloud Services ◽  
Raspberry Pi ◽  
Student Attendance ◽  
Online Student ◽  
Internet Server ◽  
Face Recognition System

This project focuses on face recognition implementation in creating fully automated attendance system with a cloud. Cloud services will provide a useful information regarding the attendance such as attendance summary performance and visualizing the data into graph and chart. In this study, we aim to create an online student attendance database, interfaced with a face recognition system based on raspberry pi 3 model B. A graphical user interface (GUI) will provide ease of use for data analysis on the attendance system. This work used open computer vision library and python for face recognition system combined with SFTP to establish connection to an internet server which runs on PHP and Node.js. The results showed that by interfacing a face recognition system with a server, a real-time attendance system can be built and be monitored remotely.  

scholarly journals Face recognition attendance system using Local Binary Pattern (LBP)

2019 ◽  
Vol 8 (1) ◽  
pp. 239-245 ◽  
Author(s):  
Shamsul J. Elias ◽  
Shahirah Mohamed Hatim ◽  
Nur Anisah Hassan ◽  
Lily Marlia Abd Latif ◽  
R. Badlishah Ahmad ◽  
...  
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Recognition System ◽  
Automated System ◽  
Student Attendance ◽  
Recognition Method ◽  
Face Recognition System ◽  
The Face

Attendance is important for university students. However, generic way of taking attendance in universities may include various problems. Hence, a face recognition system for attendance taking is one way to combat the problem. This paper will present an automated system that will automatically saves student’s attendance into the database using face recognition method. The paper will elaborate on student attendance system, image processing, face detection and face recognition. The face detection part will be done by using viola-jones algorithm method while the face recognition part will be carried on by using local binary pattern (LBP) method. The system will ensure that the attendance taking process will be faster and more accurate.

scholarly journals Face Recognition System for Indoor Applications Based on Video with the LNMF and NMFsc Methods

2020 ◽  
Vol 55 (6) ◽  
Author(s):  
Della Gressinda Wahana ◽  
Bambang Hidayat ◽  
Suci Aulia ◽  
Sugondo Hadiyoso
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Face Detection ◽  
Matrix Factorization ◽  
Computation Time ◽  
Principal Component ◽  
Recognition System ◽  
Face Recognition System ◽  
Average Accuracy ◽  
Non Negative Matrix Factorization

Face detection and face recognition are among the most important research topics in computer vision, as many applications use faces as objects of biometric technology. One of the main issues in applying face recognition is recording the attendance of active participants in a room. The challenge is that recognition through video with multiple object conditions in one frame may be difficult to perform. The Principal Component Analysis method is commonly used in face detection. Principal Component Analysis still has shortcomings: the accuracy decreases when it is applied to large datasets and performs slowly in real-time applications. Therefore, this study simulates a face recognition system installed in a room based on video recordings using Non-negative Matrix Factorization suppressed carrier and Local Non-negative Matrix Factorization methods. Data acquisition is obtained by capturing video in classrooms with a resolution of 640 x 480 pixels in RGB, .avi format, video frame rate of 30 fps, and video duration of ±10 seconds. The proposed system can perform face recognition in which the average accuracy value of the Local Non-negative Matrix Factorization method is 71.61% with a computation time of 152,634 seconds. By contrast, the average accuracy value of the Non-negative Matrix Factorization suppressed carrier method is 86.76% with a computation time of 467,785 seconds. The proposed system is expected to be used for simultaneously finding and identifying faces in real time.

scholarly journals Performance of Face Recognition System Using Gradient Laplacian Operators and New Features Extraction Method Based on Linear Regression Slope

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Abdulbasit Alazzawi ◽  
Osman N. Ucan ◽  
Oguz Bayat
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Linear Regression ◽  
Edge Detection ◽  
Extraction Method ◽  
Principal Component ◽  
Recognition System ◽  
Detection Techniques ◽  
Face Recognition System ◽  
Recognition Systems

Recent research proves that face recognition systems can achieve high-quality results even in non-ideal environments. Edge detection techniques and feature extraction methods are popular mechanisms used in face recognition systems. Edge detection can be used to construct the face map in the image efficiently, in which feature extraction techniques generate the most suitable features that can identify human faces. In this study, we present a new and efficient face recognition system that uses various gradient-and Laplacian-based operators with a new feature extraction method. Different edge detection operators are exploited to obtain the best image edges. The new and robust method based on the slope of the linear regression, called SLP, uses the estimated face lines in its feature extraction step. Artificial neural network (ANN) is used as a classifier. To determine the best scheme that gives the best performance, we test combinations of various techniques such as (Sobel filter (SF), SLP/principal component analysis (PCA), ANN), (Prewitt filter(PF), SLP/PCA, ANN), (Roberts filter (RF), SLP/PCA, ANN), (zero cross filter (ZF), SLP/PCA, ANN), (Laplacian of Gaussian filter (LG), SLP/PCA, ANN), and (Canny filter(CF), SLP/PCA, ANN). The BIO ID dataset is used in the training and testing phases for the proposed face recognition system combinations. Experimental results indicate that the proposed schemes achieve satisfactory results with high-accuracy classification. Notably, the combinations of (SF, SLP, ANN) and (ZF, SLP, ANN) gain the best results and outperform all the other algorithm combinations.

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