| _version_ | 1866901860145692672 |
|---|---|
| author | Shama S Dessai, Tharun Kumar N, Vivek M, Vikas and Yuvraj S Pujar |
| author_facet | Shama S Dessai, Tharun Kumar N, Vivek M, Vikas and Yuvraj S Pujar |
| contents | <p><span lang="EN-US">In today’s fast-paced and technologically advancing world, educational institutions and professional environments are increasingly dependent on digital systems for maintaining efficiency and accountability. One such area that demands automation and reliability is the attendance management system. Traditional methods<span> </span>of<span> </span>attendance,<span> </span>which<span> </span>include<span> </span>manual<span> </span>register<span> </span>signing,<span> </span>roll-calling,<span> </span>or<span> </span>biometric-based<span> </span>systems<span> </span>such as fingerprint scanners, are either time-consuming, susceptible to manipulation, or require physical contact—posing hygiene concerns, especially in the post-pandemic era. This research introduces an innovative and comprehensive web-based Face Recognition Attendance Management System (FRAMS) that employs the capabilities of artificial intelligence and computer vision to offer a secure, efficient, and non-intrusive solution for recording attendance.</span></p> <p><span lang="EN-US">The<span> </span>system<span> </span>is<span> </span>developed<span> </span>using<span> </span>a<span> </span>combination<span> </span>of<span> </span>Python,<span> </span>Django,<span> </span>and<span> </span>front-end<span> </span>technologies<span> </span>such<span> </span>as<span> </span>HTML, CSS, JavaScript, and Bootstrap for a fully responsive and interactive user interface. The face recognition functionality is powered by Dlib and OpenCV, two of the most robust and widely-used libraries in the computer vision domain. Dlib is used for extracting high-dimensional facial embeddings while OpenCV handles real-time camera capture and image pre-processing. The system utilizes MySQLite during local development and is scalable to MySQL for production-level database operations.</span></p> <p><span lang="EN-US">FRAMS<span> </span>introduces<span> </span>a<span> </span>dual-role<span> </span>architecture<span> </span>where<span> </span>both<span> </span>students<span> </span>and<span> </span>teachers<span> </span>can<span> </span>register,<span> </span>log<span> </span>in,<span> </span>and<span> </span>access role-specific dashboards. During student registration, users are required to submit essential academic and personal details such as username, email, password, USN (University Serial Number), department, and a facial<span> </span>image<span> </span>either<span> </span>through<span> </span>direct webcam capture<span> </span>or<span> </span>by<span> </span>uploading a<span> </span>pre-existing<span> </span>photo.<span> </span>This<span> </span>image<span> </span>is<span> </span>then processed, and<span> </span>the encoded<span> </span>facial<span> </span>features are<span> </span>stored<span> </span>in<span> </span>the<span> </span>database.<span> </span>Teachers,<span> </span>during<span> </span>registration,<span> </span>provide their name, email, password, designation, and department. Once authenticated, teachers can initiate the attendance process from their dashboards.<span> </span>This action activates the webcam which captures live footage<span> </span>of students<span> </span>present<span> </span>in<span> </span>front<span> </span>of<span> </span>the<span> </span>system.<span> </span>These<span> </span>faces<span> </span>are<span> </span>then<span> </span>compared<span> </span>in<span> </span>real-time<span> </span>with<span> </span>the<span> </span>database<span> </span>of<span> </span>pre- registered student faces. If a match is found within the set threshold, the system automatically marks the student<span> </span>as<span> </span>“Present”<span> </span>in<span> </span>the<span> </span>attendance<span> </span>log;<span> </span>otherwise,<span> </span>their<span> </span>attendance<span> </span>status<span> </span>remains<span> </span>“Absent.”<span> </span>Students have<span> </span>access<span> </span>to<span> </span>their<span> </span>attendance<span> </span>reports<span> </span>and<span> </span>can<span> </span>view<span> </span>their<span> </span>attendance<span> </span>percentage<span> </span>through<span> </span>their<span> </span>personalized <span>dashboard.</span></span></p> <p><span lang="EN-US">The primary objectives of this system are to eliminate human error, reduce administrative workload, and enhance accuracy and integrity in attendance tracking. The facial recognition component is trained to achieve a high level of accuracy under varied lighting conditions and angles to ensure reliability. Further, the<span> </span>web<span> </span>application<span> </span>is<span> </span>designed<span> </span>to<span> </span>support<span> </span>scalability,<span> </span>security<span> </span>(with<span> </span>hashed<span> </span>passwords<span> </span>and<span> </span>role-based<span> </span>access control), and performance optimization for real-time use. This paper discusses the architectural design, technology stack, data workflow, recognition pipeline, database schema, and real-time implementation of the system in detail.<span> </span>Additionally, experimental results, accuracy analysis, and user experience metrics are presented to demonstrate the feasibility and effectiveness of the proposed solution in real-world <span>environments..</span></span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15722619 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | FACE RECOGNITION ATTENDANCE MANAGEMENT SYSTEM Shama S Dessai, Tharun Kumar N, Vivek M, Vikas and Yuvraj S Pujar Face Recognition, Attendance System, Python Django, OpenCV, Dlib, MySQL <p><span lang="EN-US">In today’s fast-paced and technologically advancing world, educational institutions and professional environments are increasingly dependent on digital systems for maintaining efficiency and accountability. One such area that demands automation and reliability is the attendance management system. Traditional methods<span> </span>of<span> </span>attendance,<span> </span>which<span> </span>include<span> </span>manual<span> </span>register<span> </span>signing,<span> </span>roll-calling,<span> </span>or<span> </span>biometric-based<span> </span>systems<span> </span>such as fingerprint scanners, are either time-consuming, susceptible to manipulation, or require physical contact—posing hygiene concerns, especially in the post-pandemic era. This research introduces an innovative and comprehensive web-based Face Recognition Attendance Management System (FRAMS) that employs the capabilities of artificial intelligence and computer vision to offer a secure, efficient, and non-intrusive solution for recording attendance.</span></p> <p><span lang="EN-US">The<span> </span>system<span> </span>is<span> </span>developed<span> </span>using<span> </span>a<span> </span>combination<span> </span>of<span> </span>Python,<span> </span>Django,<span> </span>and<span> </span>front-end<span> </span>technologies<span> </span>such<span> </span>as<span> </span>HTML, CSS, JavaScript, and Bootstrap for a fully responsive and interactive user interface. The face recognition functionality is powered by Dlib and OpenCV, two of the most robust and widely-used libraries in the computer vision domain. Dlib is used for extracting high-dimensional facial embeddings while OpenCV handles real-time camera capture and image pre-processing. The system utilizes MySQLite during local development and is scalable to MySQL for production-level database operations.</span></p> <p><span lang="EN-US">FRAMS<span> </span>introduces<span> </span>a<span> </span>dual-role<span> </span>architecture<span> </span>where<span> </span>both<span> </span>students<span> </span>and<span> </span>teachers<span> </span>can<span> </span>register,<span> </span>log<span> </span>in,<span> </span>and<span> </span>access role-specific dashboards. During student registration, users are required to submit essential academic and personal details such as username, email, password, USN (University Serial Number), department, and a facial<span> </span>image<span> </span>either<span> </span>through<span> </span>direct webcam capture<span> </span>or<span> </span>by<span> </span>uploading a<span> </span>pre-existing<span> </span>photo.<span> </span>This<span> </span>image<span> </span>is<span> </span>then processed, and<span> </span>the encoded<span> </span>facial<span> </span>features are<span> </span>stored<span> </span>in<span> </span>the<span> </span>database.<span> </span>Teachers,<span> </span>during<span> </span>registration,<span> </span>provide their name, email, password, designation, and department. Once authenticated, teachers can initiate the attendance process from their dashboards.<span> </span>This action activates the webcam which captures live footage<span> </span>of students<span> </span>present<span> </span>in<span> </span>front<span> </span>of<span> </span>the<span> </span>system.<span> </span>These<span> </span>faces<span> </span>are<span> </span>then<span> </span>compared<span> </span>in<span> </span>real-time<span> </span>with<span> </span>the<span> </span>database<span> </span>of<span> </span>pre- registered student faces. If a match is found within the set threshold, the system automatically marks the student<span> </span>as<span> </span>“Present”<span> </span>in<span> </span>the<span> </span>attendance<span> </span>log;<span> </span>otherwise,<span> </span>their<span> </span>attendance<span> </span>status<span> </span>remains<span> </span>“Absent.”<span> </span>Students have<span> </span>access<span> </span>to<span> </span>their<span> </span>attendance<span> </span>reports<span> </span>and<span> </span>can<span> </span>view<span> </span>their<span> </span>attendance<span> </span>percentage<span> </span>through<span> </span>their<span> </span>personalized <span>dashboard.</span></span></p> <p><span lang="EN-US">The primary objectives of this system are to eliminate human error, reduce administrative workload, and enhance accuracy and integrity in attendance tracking. The facial recognition component is trained to achieve a high level of accuracy under varied lighting conditions and angles to ensure reliability. Further, the<span> </span>web<span> </span>application<span> </span>is<span> </span>designed<span> </span>to<span> </span>support<span> </span>scalability,<span> </span>security<span> </span>(with<span> </span>hashed<span> </span>passwords<span> </span>and<span> </span>role-based<span> </span>access control), and performance optimization for real-time use. This paper discusses the architectural design, technology stack, data workflow, recognition pipeline, database schema, and real-time implementation of the system in detail.<span> </span>Additionally, experimental results, accuracy analysis, and user experience metrics are presented to demonstrate the feasibility and effectiveness of the proposed solution in real-world <span>environments..</span></span></p> |
| title | FACE RECOGNITION ATTENDANCE MANAGEMENT SYSTEM |
| topic | Face Recognition, Attendance System, Python Django, OpenCV, Dlib, MySQL |
| url | https://doi.org/10.5281/zenodo.15722619 |