Implementation of RFID Attendance System with Face Detection using Validation Viola-Jones and Local Binary Pattern Histogram Method

Face detection and recognition are the most substantial research areas in computer vision and transfer learning due to the inspiring nature of faces as an object. In this paper, we show that we can obtain promising results on the standard face databanks when the features are extracted merely from the eye. The contributions of this work are divided into three parts, specifically face detection, eyes detection and recognition for individual identification. The key features for face recognition, used in this study are the eyes, nostrils, and mouth. The key features for eyes recognition are center of left eye, center of right eye, midpoint of eyes and extraction of eyebrows. Extracted Local Binary Pattern Histogram (LBPH) method is used to extract the facial features of face images whose computational complexity is very low and these features contain simple pixel values. Furthermore, neighborhood pixels are calculated to extract effective facial feature to realize eyes recognition and person verification. This study is able to identify an individual on the basis of even a single eye. The algorithm finds the brighter eye from the face and then, on the basis of that eye, the person is identified and the name of person is provided. The experimental results of this study show that faces are recognized accurately and LBPH method has achieved 98.2% accuracy.

Abstract— In this digital era, face recognition system plays a vital role in almost every sector. Face recognition is one of the mostly used biometrics. It can used for security, authentication, identification, and has got many more advantages. Despite of having low accuracy when compared to iris recognition and fingerprint recognition, it is being widely used due to its contactless and non-invasive process. Furthermore, face recognition system can also be used for attendance marking in schools, colleges, offices, etc. This system aims to build a class attendance system which uses the concept of face recognition as existing manual attendance system is time consuming and cumbersome to maintain. And there may be chances of proxy attendance. Thus, the need for this system increases. This system consists of four phases- database creation, face detection, face recognition, attendance updation. Database is created by the images of the students in class. Face detection and recognition is performed using Haar-Cascade classifier and Local Binary Pattern Histogram algorithm respectively. Faces are detected and recognized from live streaming video of the classroom. Keywords—Face Recognition; Face Detection; Haar-Cascade; Opencv :library are used in face recognition system.

An android based course attendance system using face recognition

Unmanned aerial vehicles are gaining a lot of popularity among an ever growing community of amateurs as well as service providers. Emerging technologies, such as LTE 4G/5G networks and mobile edge computing, will widen the use case scenarios of UAVs. In this article, we discuss the potential of UAVs, equipped with IoT devices, in delivering IoT services from great heights. A high-level view of a UAV-based integrative IoT platform for the delivery of IoT services from large height, along with the overall system orchestrator, is presented in this article. As an envisioned use case of the platform, the article demonstrates how UAVs can be used for crowd surveillance based on face recognition. To evaluate the use case, we study the offloading of video data processing to a MEC node compared to the local processing of video data onboard UAVs. For this, we developed a testbed consisting of a local processing node and one MEC node. To perform face recognition, the Local Binary Pattern Histogram method from the Open Source Computer Vision is used. The obtained results demonstrate the efficiency of the MEC-based offloading approach in saving the scarce energy of UAVs, reducing the processing time of recognition, and promptly detecting suspicious persons.

After the breakout of the worldwide pandemic COVID-19, there arises a severe need of protection mechanisms, face mask being the primary one. According to the World Health Organization, the corona virusCOVID-19 pandemic is causing a global health epidemic, and the most successful safety measure is wearing a face mask in public places. Convolutional Neural Networks (CNNs) have developed themselves as a dominant class of image recognition models. The aim of this research is to examine and test machine learning capabilities for detecting and recognize face masks worn by people in any given video or picture or in real time. This project develops a real-time, GUI-based automatic Face detection and recognition system. It can be used as an entry management device by registering an organization’s employees or students with their faces, and then recognizing individuals when they approach or leave the premises by recording their photographs with faces.The proposed methodology makes uses of Principal Component Analysis (PCA) and HAAR Cascade Algorithm. Based on the performance and accuracy of our model, the result of the binary classifier will be indicated showing a green rectangle superimposed around the section of the face indicating that the person at the camera is wearing a mask, or a red rectangle indicating that the person on camera is not wearing a mask along with face identification of the person. Face detection and face recognition are very important technologies these days, furthermore we noticed that they got have a variety of uses such as cellphones, army uses, and some high risk information offices. We decided to make a device that detects and recognize the face as a student attendance system and can be a substitute for the regular paper attendance system and finger print attendance system. The main function in our project is going to be done using LabVIEW because, LabVIEW is a very helpful programming tool in regards of facial uses and very helpful in other uses. Our project is based on a main program in LabVIEW that detects and recognize faces with giving scores and parameters, furthermore the subsystems are an Excel sheet that is integrated with the program, and a messaging device that is for either a message for absent students or to the student’s parent.

In this work we propose a Live Attendance Marking System for institutional purpose. This system will enable the department to mark the attendance of students automatically by recognizing their faces. The system is based on the face detection and recognition algorithms and automatically recognizes a student whenever he/she comes across the camera module. Further after the recognition, it automatically updates his/her attendance in the Cloud web server database. The basic architecture and all the algorithms used in the model are described in the paper elaborately. Moreover in order to evaluate and enhance the performance of the system many of the filter functions are used which provide the capability to capture and recognize the images even in dull and low light places. This project also suggests the technique to ensure that a student can mark the attendance only once in a day. The live attendance system is much more efficient to traditional attendance systems both in saving time and in maintaining the database.

In the traditional system, it is difficult to handle the attendance of a huge number of students in the classroom. Attendance of the students is very important for every university, college as well as school as it keeps the record of every individual. The conventional methodology of taking attendance by calling out the roll number or name of the student and then recording the attendance is a tedious task. Assume the duration of the lecture is 60 minutes or 1 hour and to record the attendance it would take 5 to 10 minutes. Also for every tutor, this is time-consuming. To overcome this problem, real-time face recognition is a real-world solution, which will help in marking the attendance of students in bulk. In this project, face detection and face recognition are used. Attendance marking through face recognition can be implemented in the classroom by capturing the image of students in the classroom via a surveillance camera. Later Haar Cascade algorithm is used for face detection, which helps in locating the position of the face region, and LBPH (Local binary pattern histogram) algorithm for face recognition. The images of all the students in the class are stored in the database and when the face of the individual student matches with one of the faces stored in the database then the attendance is recorded

Achieving low recognition time and high accuracy in real-time face recognition is challenging. This study implements Viola-Jones, Local Binary Pattern Histogram, and Euclidean Distance for real-time face recognition and calculates the face detection time. The face image is detected using the Viola-Jones method; its features are extracted using the Local Binary Pattern Histogram, and the face is recognized using Euclidean Distance. This study processes sample images from 1013 students as training data, with 20 images represent each student. The experiments show that 268 of 342 testing data are recognized correctly, resulting in an accuracy of 78.4%, with average real-time recognition time of 0.93 seconds.

The local binary pattern histogram (LBPH) algorithm is a computer technique that can detect a person's face based on information stored in a database (trained model). In this research, the LBPH approach is applied for face recognition combined with the embedded platform on the actuator system. This application will be incorporated into the robot's control and processing center, which consists of a Raspberry Pi and Arduino board. The robot will be equipped with a program that can identify and recognize a human's face based on information from the person's eyes and nose. Based on the results of facial feature identification testing, the eyes were recognized 131 times (87.33%), and the nose 133 times (88.67%) out of 150 image data samples. From the test results, an accuracy rate of 88%, the partition rate of 95.23%, the recall of 30%, the specificity of 99%, and the F1-Score of 57.5% were obtained.

Attending classes by students is associated with the assimilation of educational material by students and the ability to plan and organize activities. However, at present in educational institutions, as a rule, student attendance is recorded manually. Activities are performed frequently and repeatedly, thus wasting instructors' study time. Additionally, the face is one of the most widely used biometric characteristics for personal identification so an automated attendance system using face recognition has been proposed. In recent years, convolutional neural networks (CNN) have become the dominant deep le11arning method for face recognition. In this article, the features of building an automated student attendance system by biometric face recognition using the convolution neural network modelhas been discussed. Analyzed and solved the main tasks that arise when building an automated student attendance monitoring system: creating a dataset of students' face images; building and training a biometric face recognition model; face recognition from the camera and registration in the database; extension to the face image dataset. The use of the capabilities of the Python and OpenCV libraries is shown. The conducted testing of the accuracy of the developed CNN model of biometric face recognition showed good results – the overall accuracy score is not less than 0.75. The developed automated student attendance monitoring system in classrooms can be used to determine student attendance in different forms of the educational process. Its implementation will significantly reduce the monitoring time and reduce the number of errors in maintaining attendance logs. The introduction of an automated attendance monitoring system will significantly improve the organization of the educational process to ensure its quality.

The frictionless, effective, and impenetrable character of facial recognition-based attendance systems is driving their increasing deployment. The automatic collection and preparation of facial image data appropriate for machine learning model training in real-time biometric attendance systems is the main goal of this study. The system uses webcam input to record and retain many face samples of a subject, utilising Haar Cascade classifiers for face detection. To guarantee uniformity and effectiveness in data collecting, crucial methods such frame sampling, region-of-interest cropping, scaling, and greyscale conversion are used. Python's OpenCV and pickle libraries are used to save the collected images in binary and image formats for further use in facial recognition algorithms. The suggested solution provides a solid basis for intelligent attendance systems in business and educational settings by minimising manual intervention and guaranteeing the creation of high-quality facial data. In the early phases of developing a machine learning pipeline for facial recognition, this real-time facial dataset generator is essential.

Face recognition is among the most productive image processing applications and has a pivotal role in the technical field. In the recent time automated face recognition has become a trend and has been developed very much, this is mainly due to two reasons first it is due to availability of modern technologies and second is due to the ability to save time using face recognition in the process of taking attendance of students. Its usage will grow vast in the future as it saves a lot of time. It consumes a lot of time to take attendance manually and few might also fake the attendance, in order to prevent time consumption and avoid faking the attendance. Face recognition is used to identify the person present in the class and mark their attendance , this is done with the help of image or video frame. We proposed an automatic attendance management system using techniques such as PCA algorithm. The face detection and recognition will automatically detect the students in the classroom and mark the attendance by recognizing the person. The traditional process of making attendance and present biometric systems are vulnerable to proxies. This paper is therefore proposed to tackle all these problems. The proposed system makes the use of Haar classifiers, KNN, CNN, SVM, Generative adversarial networks, and Gabor filters. After face recognition attendance reports will be generated and stored in excel format. The system is tested under various conditions like illumination, head movements, the variation of distance between the student and cameras. After vigorous testing overall complexity and accuracy are calculated. The Proposed system proved to be an efficient and robust device for taking attendance in a classroom without any time consumption and manual work. The system developed is cost-efficient and need less installation

Traditional attendance systems, such as manual sign-ins or RFID-based methods, are time- consuming, prone to errors, and susceptible to manipulation. This paper presents a Smart Attendance System that leverages facial recognition technology to automate attendance tracking. The system integrates advanced face detection and recognition algorithms, along with anti-spoofing mechanisms, to ensure secure and efficient attendance marking. The proposed system uses the OpenCV library for face detection, the FaceNet algorithm for facial recognition, and Fourier Spectrum Analysis for anti-spoofing. Built with Python and Tkinter, the system provides a user-friendly interface for real-time attendance logging and management. The results demonstrate high accuracy in face recognition, efficient processing, and robust security against spoofing attempts, making it a reliable solution for modern attendance management in educational and corporate environments.

Real-time face detection and recognition using hybrid-information extracted from face space and facial features

Nowadays, the fourth industrial revolution has achieved significant advancement in high technology, in which artificial intelligence has had vigorous development. In practice, facial recognition is one most essential tasks in the field of computer vision with various potential applications from security and attendance system to intelligent services. In this paper, we propose an efficient deep learning approach to facial recognition. The paper utilizes the architecture of improved FaceNet model based on MobileNetV2 backbone with SSD subsection. The improved architecture uses depth-wise separable convolution to reduce the model size and computational volume and achieve high accuracy and processing speed. To solve the problem of identifying a person entering and exiting an area and integrating on advanced mobile devices limits to (such as limited memory and on-device storage) highly mobile resources. Especially, our approach yields better results in practical application with more than 95% accuracy on a small dataset of the original face images. Obtained frame rate (25 FPS) is very favorable compared to the field of facial recognition using neural network. Besides, the deep learning based on solution could be applicable in many low-capacity hardware or optimize system’s resource. Finally, the smart automated attendance systems is successfully designed basing on the improved efficient facial recognition.