Automated Face Recognition based Attendance System using RetinaFace and FaceNet

This paper introduces a cutting-edge face recognition-based attendance system, designed to address the limitations of traditional attendance methods through the integration of advanced machine learning, computer vision, and geospatial APIs. The system streamlines the attendance process by automating the identification and logging of attendees with high accuracy and efficiency. Key features include live video recognition for real-time face identification, an intuitive user registration module for enrolling new individuals, CSV-based logging for seamless data export and management, and geolocation-aware attendance tracking to ensure that records are not only time-accurate but also location-specific. This geospatial context provides valuable insights, particularly for distributed teams or multi-location setups. The implementation leverages Python, a versatile programming language, and integrates OpenCV for real-time video processing and face detection, ensuring quick and reliable face recognition even in dynamic environments. The graphical user interface (GUI) is developed using PyQt5, allowing for a user-friendly and responsive experience. This combination of powerful technologies ensures that the system is both scalable and adaptable, able to integrate easily into various organizational workflows, from small educational institutions to large-scale corporate environments. The system's practical application is validated through experimental results conducted in diverse settings, including workplaces, academic institutions, and security-sensitive environments. These results highlight the system’s exceptional accuracy, even under challenging conditions such as low lighting or crowded spaces. Furthermore, the system demonstrates its potential to enhance operational efficiency, reduce administrative overhead, and improve security by providing a reliable, context-aware solution for attendance management. In conclusion, this face recognition-based attendance system offers a modern, automated solution that combines the power of machine learning and computer vision with geospatial data, creating an intelligent, highly effective tool for attendance tracking across a wide range of industries and applications.

Objectives: Face recognition has arisen as a smart solution to discourse many present-day needs for empathy and the confirmation of identity claims. It fetches together the capacity of other biometric methods, which endeavor to tie uniqueness for the individual characteristic features of the body, and the further familiar functionality of visual reconnaissance systems. Face acknowledgment is a vital field for authentication purpose particularly in the case of student's attendance. This paper is intended at applying a digitized system for attendance recording. Methods/Statistical Analysis: In this process two methods are used to determine the face recognition attendance system- Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA). PCA is a statistical procedure that uses an orthogonal modification to transform a set of observations of probably correlated variables into a set of values of linearly uncorrelated variables. LDA is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition and machine learning to find a linear permutation of features that shows or separates two or more classes of objects or events. Findings: However the current system of tracking attendance via records is difficult to manage. Face recognition based attendance system deals with the maintenance of the student's daily attendance details. Biometric appreciation comprises alike, within an open-mindedness of calculation, of pragmatic biometric behaviors in contradiction of formerly poised data for a focus. Estimated identical is obligatory due to the dissimilarities in biological characteristics and deeds both within and among persons. It generates the attendance for the student on basis of presence in the class. Application/Improvements: Project can be modernized in nearby future as, when a responsibility for the same arises, as it is very flexible in positions of growth. And the enrichment approach of camera formation based on the result of the position valuation in order to progress the face detection effectiveness.

The purpose of this research is to design and implement an attendance system based on internet of things (IoT) . The proposed system integrated two types of attendance systems, face recognition based attendance system (FRA) and fingerprint-based attendance system (FPA), with a central server. The FRA was developed in a Raspberry Pi mini-computer using Python programming language and Openc CV library. The FPA, on the other hand, was developed using Node MCU ESP8266 and Fingerprint scanner AS608 with Adafruit Fingerprint library. Both FRA and FPA are connected to a web server with a database engine through the internet connection and sensing attendance data using the HTTP_POST method. The server was developed using Apache Webserver, PHP programming language, and MySQL database engine. The server serves two main purposes, which are to record the attendance data sent by the FPA and FRA, and generate an attendance report based on the user query. The system testing was done in a local network. The result showed that both the subsystems and the integrated system worked well

Traditional attendance management systems relied heavily on manual methods such as roll calls, sign-in sheets, or ID card swiping. These conventional approaches were prone to human errors, time-consuming processes, and the risk of proxy attendance, where one individual marks attendance on behalf of another. Additionally, biometric methods like fingerprint scanners, though more secure, faced hygiene concerns, especially during public health crises, and suffered from device wear and tear. This paper presents an intelligent and automated Face Recognition-Based Attendance System leveraging machine learning techniques and Euclidean distance matching for accurate identity verification. In this product the niche feature is QR code scanning. All the registered students will have their name called upon scanning the QR code, followed by their live face verification. Only after both IN and OUT verification the student attendance is recorded by the APP. The system is also developed using a combination of VB.NET, C\#, Python, and MySQL for back-end integration, with XAMPP used for local server handling and database connectivity. This allows the flexibility to deploy the project as either a web application or mobile application. This, in turn, makes the product economical. Hence, it can be deployed in any sector of society. The integration of multiple platforms along with the machine learning model has led to the accuracy of 92 %. As the model is trained, the accuracy is expected to achieve 100 %. With the existing system having specifications of Intel Core i3 processor, 8GB RAM, and Windows 10 OS, the time required for attendance is 1.42 sec approx. This system aims to eliminate proxy attendance, reduce manual work, and enhance reliability in academic and professional environments.

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

This research aims to design and implement an Internet of Things (IoT) based attendance system. The designed system integrates two types of attendance systems, face recognition-based attendance system (FRA) and fingerprint-based attendance system (FPA), with a server. FRA uses Python programming language and Openc CV library, while FPA is developed using ESP8266 MCU Node and AS608 Fingerprint scanner with Adafruit Fingerprint library. FRA and FPA are connected to a web server with a database engine via an internet connection and send attendance data using the HTTP_POST method. The server development uses Apache Webserver, PHP programming language, and MySQL database engine. The server has two primary purposes: to record attendance data sent by FPA and FRA and to generate attendance reports based on user requests. System testing is done on a local network. The results show that the subsystems and integrated systems work well.

Aims: The rise of automation has greatly impacted various aspects of daily life, including attendance management systems. Traditional methods like manual roll calls and card swiping are often inefficient and prone to errors, leading researchers to explore innovative alternatives such as face recognition technology. This technology employs advanced algorithms and image processing techniques to accurately identify individuals based on their unique facial features, thereby enhancing accuracy and security measures. The objective of this project is to develop a face recognition-based attendance system with specific goals, including circuit design, simulation using Proteus Software, implementation, and performance evaluation through rigorous field-testing. Study Design: The system comprises an input subsystem with a Camera module and two output subsystems: an LCD and a Web-database PC software. Initially, custom and stored image data of students are saved in a microSD card of the camera module, and attendance records are updated on a web-database upon successful scanning and recognition of students' faces. Methodology: The implementation process involves assembling various hardware components, including securely mounting the ESP32 CAM and integrating a 16x4 Character LCD Display for visual feedback. LEDs are used as visual indicators for system statuses, while a Power Bank Module ensures consistent power supply. The system is controlled by the ESP32-CAM, which captures and verifies faces, displays actions on the LCD, and updates the web-database with attendance data. Results: Testing results indicate the system's high accuracy, achieving a 100% attendance rate across multiple class sessions with zero misclassifications. Conclusion: The user-friendly interface and seamless wireless connectivity enhance accessibility and real-time monitoring, making it a valuable tool for classroom management and office use. The research has some hardware limitations which includes processing large amounts of facial data requires robust hardware. Inadequate camera resolution or processing power can lead to slow performance or inaccurate recognition. Overall, the face recognition-based attendance system has the potential to revolutionize traditional methods, offering high accuracy, user-friendly features, and robust connectivity, with possible applications beyond education through further refinement and research. Facial recognition-based attendance systems are more accurate and efficient than traditional methods like manual roll calls or card swiping. By automating attendance, they reduce errors and provide real-time updates with web integration. Despite hardware limitations, the system's accuracy and ease of use make it a superior alternative with potential for broader applications.

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.

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.

An android based course attendance system using face recognition

In this study, attendance system was created by combines RFID attendance technology and face recognition. The attendance system is an important thing to facilitate attendance data retrieval. Attendance system often encountered in the technological era such as today is the RFID attendance system. Although the attendance system is already sophisticated, this system still has shortcomings such as the occurrence of entrusted cards. In this research, the writer has two inputs, namely ID on the RFID card and a face image. Face images will be process by two methods there are the Viola-Jones method that use to detecting the face objects in the image, and the Local Binary Pattern Histogram method as face recognition. Furthermore, if the face was recognized then the RFID card ID data and face recognition data will compare, the results of this process will be attended data in the database. Based on the results of test have been done by the author, the response system shows very good results. In the first test of the RFID sensor in 11 tests, the sensor was able to distinguish all cards that had been registered and cards that had not been registered. This test is carried out from distance of 1 cm to 4 cm. Furthermore, in the second test of facial recognition, in 100 trials of facial recognition testing with different facial poses, the system can recognize faces 93 times so system has 93% success rate. This success rate is influenced by several factors such as the test carried out at 364 lux illumination strength, the test distance at $\gt $ 20cm and $\lt $70cm, as well as the maximum testing angle 45°.

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

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.

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.