Query: Attendance systemface recognition

title: An improved face recognition algorithm and its application in attendance management system
abstract: Face Recognition is a computer application that is capable of detecting, tracking, identifying or verifying human faces from an image or video captured using a digital camera. Although lot of progress has been made in domain of face detection and recognition for security, identification and attendance purpose, but still there are issues hindering the progress to reach or surpass human level accuracy. These issues are variations in human facial appearance such as; varying lighting condition, noise in face images, scale, pose etc. This research paper presents a new method using Local Binary Pattern (LBP) algorithm combined with advanced image processing techniques such as Contrast Adjustment, Bilateral Filter, Histogram Equalization and Image Blending to address some of the issues hampering face recognition accuracy so as to improve the LBP codes, thus improve the accuracy of the overall face recognition system. Our experiment results show that our method is very accurate, reliable and robust for face recognition system that can be practically implemented in real life environment as an automatic attendance management system.

title: Real Time Automatic Attendance System for Face Recognition Using Face API and OpenCV
abstract: Traditionally, the attendance of students has been a major concern for the colleges and the faculty has to spend a lot of time and is a tedious job to mark attendance manually. Current biometric attendance system is not automatic that's why wastes a lot of time, difficult to maintain and requires a queue for scanning fingerprints to mark their attendance. In Modern era everyone has Smartphone and connected via internet every time. In this paper attendance monitoring will be done through smart phone available with almost all faculty members. Some of popular object detection algorithms are back propagation neural network, region based convolution network (RCNN) faster RCNN, single shot detector. Our unified structure is based on YOLO V3 (You only look once) algorithm for face detection and Microsoft Azure using face API for face recognition (face database) The unique part is camera installed in classroom will take picture twice one at the start and one at the end to ensure students has attended complete class. YOLO V3 will first count the students in an image followed by identifying faces as known and unknown generating spreadsheets separately and an email is send at the end of month to students, parents and faculty. The designed system performs efficient in real time implementation for counting and detection. Our entire system has proven to gather high accuracy in face detection and performance.

title: Student Smart Attendance Through Face Recognition using Machine Learning Algorithm
abstract: In today's competitive world, with very less classroom time and increasing working hours, lecturers may need tools that can help them to manage precious class hours efficiently. Instead of focusing on teaching, lecturers are stuck with completing some formal duties, like taking attendance, maintaining the attendance record of each student, etc. Manual attendance marking unnecessarily consumes classroom time, whereas smart attendance through face recognition techniques helps in saving the classroom time of the lecturer. Attendance marking through face recognition can be implied in the classroom by capturing the image of the students in the classroom via the camera installed. Later through the HAAR Cascade algorithm and MTCNN model, face region needs to be taken as interest and the face of each student is bounded through a bounding box, and finally, attendance can be marked into the database based on their presence by using Decision Tree

title: Implementing CCTV Based Attendance Taking Support System Using Deep Face Recognition: A Case Study at FPT Polytechnic College
abstract: Face recognition (FR) has received considerable attention in the field of security, especially in the use of closed circuit television (CCTV) cameras in security monitoring. Although significant advances in the field of computer vision are made, advanced face recognition systems provide satisfactory performance only in controlled conditions. They deteriorate significantly in the face of real world scenarios such as lighting conditions, motion blur, camera resolution, etc. This article shows how we design, implement, and conduct the empirical comparisons of machine learning open libraries in building attendance taking (AT) support systems using indoor security cameras called ATSS. Our trial system was deployed to record the appearances of 120 students in five classes who study on the third floor of FPT Polytechnic College building. Our design allows for flexible system scaling, and it is not only usable for a school but a generic attendance system with CCTV. The measurement results show that the accuracy is suitable for many different environments.

title: Face Recognition based Attendance System using Haar Cascade and Local Binary Pattern Histogram Algorithm
abstract: The attendance system is used to track and monitor whether a student attends a class. There are different types of attendance systems like Biometric based, Radiofrequency card based, face recognition based and old paper based attendance system. Out of them all, a Face recognition based attendance system is more secure and time saving. There are several research papers focusing on only the recognition rate of students. This research focusing on a face recognition based attendance system with getting a less false positive rate using a threshold to confidence i.e. euclidean distance value while detecting unknown persons and save their images. Compare to other euclidean distance based algorithms like Eigenfaces and Fisherfaces, Local Binary Pattern Histogram (LBPH) algorithm is better [11] We used Haar cascade for face detection because of their robustness and LBPH algorithm for face recognition. It is robust against monotonic grayscale transformations. Scenarios such as face recognition rate, false positive rate for that and false positive rate with and without using a threshold in detecting unknown persons are considered to evaluate our system. We got face recognition rate of students is 77% and its false positive rate is 28% This system is recognizing students even when students are wearing glasses or grown a beard. Face Recognition of unknown persons is nearly 60% for both with and without applying threshold value. Its false positive rate is 14% and 30% with and without applying threshold respectively.

title: Development of an Automatic Class Attendance System using CNN based Face Recognition
abstract: We are living in the 21st century which is the era of modern technology. Many traditional problems are being solved using new innovative technologies. Taking attendance daily is an indispensable part of educational institutions as well as offices. It is both exhausting and time consuming if done manually. Biometric attendance systems through voice, iris, and fingerprint recognition require complex and expensive hardware support. An auto attendance system using face recognition, which is another biometric trait, can resolve all these problems. This paper represents the development of a face recognition based automatic student attendance system using Convolutional Neural Networks which includes data entry, dataset training, face recognition and attendance entry. The system can detect and recognize multiple person's face from video stream and automatically record daily attendance. The proposed system achieved an average recognition accuracy of about 92 Using this system, daily attendance can be recorded effortlessly avoiding the risk of human error.

title: PRESENT: An Android Based Class Attendance Monitoring System Using Face Recognition Technology
abstract: The study aimed to develop an Android Based Class Attendance Monitoring Application using Face Recognition to make attendance checking and monitoring easier and faster. In addition, the system has also integrated the use of SMS technology to notify parents/guardians whenever the students attended a particular subject or class. In addition, the study also intended to evaluate the developed application in terms of functionality, usability, reliability, and portability.  The researcher utilized the incremental model. After the development phase, the application was evaluated by seventeen (17) faculty members from the College of Engineering and Computing Sciences. A validated evaluation questionnaire was used to rate the level of acceptability of the application based on ISO 9126 software quality and the level of satisfaction for its major features. For the statistical treatment of the data collected, Likert Scale, weighted mean and t test were utilized by the researcher.  The results revealed that instructors find the existing way of checking attendance as time consuming and a tedious task. Furthermore, the respondents assessed the developed application as moderately acceptable in terms of functionality, reliability and usability while portability was rated as highly acceptable. With regards to the features, the respondents were very satisfied. The t test also revealed that there is a significant difference between the level respondents' level of acceptability on the existing and the proposed system.  The researcher concluded that the developed application was useful and it can support the needs of the instructors to make attendance checking and monitoring easier, faster, and reliable. Recommendations Due to its acceptable evaluation result, instructors should consider the use of this tool as an alternative to the existing process of checking and monitoring class attendance. The developed system can be enhanced in terms of user design to make it more user friendly.  Classroom presence is important because this helps students to succeed in their academics when they attend class regularly. With the integration of different technologies such as Android, face recognition and SMS, the traditional way of checking class attendance can be made easier, faster, reliable and secured, thus improving classroom management.

title: Smart Attendance System using OPENCV based on Facial Recognition
abstract: Face is the crucial part of the human body that uniquely identifies a person. Using the face characteristics as biometric, the face recognition system can be implemented. The most demanding task in any organization is attendance marking. In traditional attendance system, the students are called out by the teachers and their presence or absence is marked accordingly. However, these traditional techniques are time consuming and tedious. In this project, the Open CV based face recognition approach has been proposed. This model integrates a camera that captures an input image, an algorithm for detecting face from an input image, encoding and identifying the face, marking the attendance in a spreadsheet and converting it into PDF file. The training database is created by training the system with the faces of the authorized students. The cropped images are then stored as a database with respective labels. The features are extracted using LBPH algorithm.

title: A Real time Attendance System Using Deep learning Face Recognition
abstract: Attendance check plays an important role in classroom management. Checking attendance by calling names or passing around a sign in sheet is time consuming, and especially the latter is open to easy fraud. This paper presents the detailed implementation of a real time attendance check system based on face recognition and its results. To recognize a student's face, the system must first take and save a picture of the student as a reference in a database. During the attendance check, the web camera takes face pictures for a student to be recognized, and then the computer automatically detects the face and identifies a student name who most likely matches the pictures, and finally an excel file will be updated for attendance record based on the face recognition results. In the system, a pre trained Haar Cascade model is used to detect faces from web camera video. A FaceNet, which has been trained by minimizing the triplet loss, is used to generate a 128dimensional encoding for a face image. The similarity between the encodings of two face images determines whether the two face images coming from the same students. The system has been used for a class, and the results are very satisfactory. A survey has been conducted to investigate the pros and cons of the attendance system on college education management.

title: Real Time Attendance System Using Face Recognition Technique
abstract: Authentication plays a very important role in computer based communication for having system control. Face recognition has become a very crucial part of biometric verification and finds uses in many applications, e.g. video monitor system, human computer interaction, network security. This paper is about integrating face recognition technology with open source computer vision (OpenCV) algorithm and develop a Attendance System. This software will facilitate the attendance automaton process and enable faculties to enquire student's data by just maintaining a log for clock in and clock out time.