{"id":23493497,"url":"https://github.com/harshd23/attendance_system_using_face_recognition","last_synced_at":"2026-05-18T04:07:56.055Z","repository":{"id":172119300,"uuid":"634489656","full_name":"harshd23/Attendance_System_using_Face_Recognition","owner":"harshd23","description":"The purpose of this Attendance System Using Face System is to record the presence or attendance of employee through a browser by recognizing the faces captured through a webcam. 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The construction of a web application to support a variety\nof system use cases, including the registration of new employees, the contribution of images to the\ntraining dataset, the viewing of attendance records, etc., is also covered in this paper, which also\ncovers issues like facial detection, alignment, and identification. The system utilizes advanced\nimage processing and artificial intelligence algorithms to recognize individuals' faces and\nautomatically mark their attendance. This technology provides several benefits over traditional\nattendance methods, including improved accuracy, efficiency, and convenience. It can also reduce\nthe risk of fraudulent activities such as proxy attendance. This project aims to be a productive\nreplacement for outdated manual attendance systems. When security is a priority, it can be\nemployed in corporate offices, educational institutions, and other organizations.  \n\n## PROBLEM STATEMENT :\n\nThe traditional methods of taking attendance in educational institutions and corporate offices, such\nas paper-based sign-in sheets or manual roll-calls, are time-consuming, inefficient, and prone to\nerrors. These methods can also be easily manipulated, leading to inaccurate attendance records and\npotential fraudulent activities, such as proxy attendance. Additionally, during the ongoing COVID-\n19 pandemic, traditional attendance methods pose a risk of spreading the virus due to physical\ncontact.  \nTo address these challenges, an attendance system using face recognition technology can provide\na viable solution. This system can automatically recognize and verify individuals' faces, reducing\nthe need for manual intervention and eliminating the risk of fraudulent activities. Moreover, there\nis a need for a comprehensive evaluation of the system's effectiveness, reliability, and scalability\nto ensure its suitability for various applications and settings.  \n\n## SCOPE AND IMPORTANCE :\n\nIn modern society, facial recognition is becoming more prevalent. In the area of security, it has\nachieved significant advancements. It is a very useful tool that can assist law enforcement in\nidentifying criminals, and software providers are utilizing the technology to make it easier for\npeople to access the technology. This technology can be improved to be used in several\ncontexts, such as ATMs, accessing private files, or handling other delicate materials.\nThe traditional attendance system, where attendance is manually recorded, will be automated\nas part of this project. Additionally, it enables an organization to digitally preserve its\nattendance, break time, in-time, and out-of-time data. The system's digitization would also aid\nin a better data visualization employing graphs to show the number of personnel now present,\ntheir cumulative work hours, and their break times. With its new features, the conventional\nattendance system is effectively upgraded and replaced.\n\n## DESIGN :\nIt is a collection of processes that facilitate the designing, development, implementation and maintenance of enterprise data management systems. It helps produce database systems:\n\n- That meet the requirements of the users.\n- Have high performance.\n\n### FLOWCHART\n\n![Flowchart](./assets/Flowchart.png \"Flowchart of the attendance system\")\n\n### TRAINING DATA FOR THE SVM ALGORITHM :\n\n![Training data](./assets/Training_data.png \"TRAINING DATA FOR THE SVM ALGORITHM\")\n\n\n## GUI SCREENSHOTS :\n\n![Admin Dashboard]( ./assets/Admin.png)\n\u003cp align=\"center\"\u003eAdmin Dashboard\u003c/p\u003e\n\n![Register Window]( ./assets/Register_Window.png)\n\u003cp align=\"center\"\u003eRegister Window\u003c/p\u003e\n\n![Homepage]( ./assets/Homepage.png)\n\u003cp align=\"center\"\u003eHomepage\u003c/p\u003e\n\n![Stats]( ./assets/Stats.png)\n\u003cp align=\"center\"\u003ePrevious Week Data in Graph\u003c/p\u003e\n\n## CONCLUSION :\n\nAn attendance system using face recognition is a powerful tool that can streamline attendance\nmanagement and improve security in various settings such as schools, workplaces, and events. The\nsystem works by capturing facial images of individuals and using machine learning algorithms\nsuch as SVM to identify and track attendance.\n\n## STEPS TO RUN THIS APPLICATION :\n1. Please install [Python version 3.8.10](https://www.python.org/downloads/release/python-3810/) to run the project successfully.\n2. It is necessary to install the [**CMake**](https://cmake.org/download/) and [**Visual Studio**](https://visualstudio.microsoft.com/downloads/).\n3. Create a folder in your PC and Clone this project in it.\n4. To run the project, Open the terminal and run the following commands:\n ```js\n  python manage.py migrate\n ```\n ```js\n  python manage.py runserver\n ```\n5. If everything is okay with your project, Django will start running the server at `localhost port 8000` (127.0. 0.1:8000) and then you have to navigate to that link in your browser.\n6. **For Admin login, the credentials are Username - admin \u0026 Password - admin **\n7. Also, To view the Database please download [**DB Browser for SQLite**](https://sqlitebrowser.org/dl/).\n8. You can then manually change the password for the employee and admin using the DB Browser.\n9. While successfully running the project, Login using Admin credentials then for Adding new employees you need to first register them as *New Employee* and then *Add Photos* of the Employee. It will take 20 seconds to capture the photos and then you can *Train the model*.\n10. You need to then Go to the landing/home page and *Mark their attendance*, to check whether the model can detect the New Employee with the name and accuracy. Similary, you can add new employees and train the model.\n11. **Note:** It is necessary to Train the model everytime after new employee have been added. \n12. Congratulations!!✨ You have successfully run the project.\n\n## CONTRIBUTING :\n\nThis is an open source project, and contributions of any kind are welcome and appreciated. Open issues, bugs, and feature requests are all listed on the [issues](https://github.com/harshd23/Attendance_System_using_Face_Recognition/issues) tab and labeled accordingly. Feel free to open bug tickets and make feature requests.\n\n## CONTRIBUTORS :\n\n- [Sarvesh Chavan](https://github.com/sarvesh2847)\n- [Harsh Dalvi](https://github.com/harshd23)\n- [Osama Shaikh](https://github.com/Osamashaikh90)\n- [Bhanu Sunka](https://github.com/Bhanu1776)\n\n\u003chr\u003e\n\n© 2023 Harsh Dalvi and contributors  \nThis project is licensed under the [**MIT license**](https://github.com/harshd23/Attendance_System_using_Face_Recognition/blob/main/LICENSE).\n\n[![forthebadge](https://forthebadge.com/images/badges/built-with-love.svg)](https://forthebadge.com)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharshd23%2Fattendance_system_using_face_recognition","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fharshd23%2Fattendance_system_using_face_recognition","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharshd23%2Fattendance_system_using_face_recognition/lists"}