In many institutions, attendance is still managed manually, which often leads to delays, errors, and issues such as proxy attendance. To overcome these limitations, this paper presents a Face Recognition Attendance System that automates the entire process using facial recognition technology.
The system is built using web technologies such as HTML, CSS, JavaScript, PHP, and MySQL. It captures a user’s image through a camera, identifies the face, and compares it with stored records to mark attendance automatically.
This method not only reduces manual effort but also improves accuracy and ensures secure data handling. The results demonstrate that the system offers a more efficient and dependable alternative to traditional attendance methods.
Keywords
Face Recognition, Automated Attendance System, Web-Based Application, face-api.js, TensorFlow.js, Deep Learning, Facial Feature Extraction, Real-Time Processing, Image Processing, Secure Authentication, Database Management, Digital Attendance System.
Conclusion
The development and implementation of the Smart_Face attendance system successfully demonstrate how modern web-based AI technologies can revolutionize traditional administrative workflows. By integrating the face-api.js library with a robust PHP and MySQL backend, this research has produced a contactless, secure, and highly cost-effective solution for real-time identity verification.
Unlike traditional biometric systems that rely on expensive, proprietary hardware, this project proves that high-level Artificial Intelligence capabilities—specifically deep learning-based facial recognition—can be deployed effectively within a standard web browser. The system effectively eliminates the long-standing issues of “proxy attendance” and manual data entry errors, ensuring that attendance records are both accurate and tamper-proof. Ultimately, Smart_Face provides a scalable architecture that bridges the gap between complex computer vision research and practical, everyday institutional utility, offering a dependable alternative to manual registration.
By integrating facial recognition with web technologies, the system becomes reliable, scalable, and suitable for real-world applications. It effectively eliminates proxy attendance and enhances overall system efficiency [18][19].
References
1)Zhao W, Chellappa R, Phillips PJ, Rosenfeld A. Face Recognition: A Literature Survey. ACM Computing Surveys.
2)Turk M, Pentland A. Eigenfaces for Recognition. Journal of Cognitive Neuroscience.
3)Viola P, Jones M. Rapid Object Detection using a Boosted Cascade of Simple Features. IEEE Conference on Computer Vision and Pattern Recognition, 2001.
4)Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT Press, 2016.
5)Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Networks. NIPS, 2012.
6)Szeliski R. Computer Vision: Algorithms and Applications. Springer, 2022.
7)Rim D, Hassan K, Pal CJ. Semi-Supervised Learning for Wild Faces and Video. BMVC, 2011.
8)Afanasyeva ZS, Afanasyev AD. Signature Detection and Identification using CNN, NumPy and OpenCV. Springer, 2020.
9)Masud M et al. Deep Learning-Based Intelligent Face Recognition in IoT-Cloud Environment. Computer Communications, 2020.
10)Jain AK, Li SZ. Handbook of Face Recognition. Springer, 2011.
11)Brunelli R, Poggio T. Face Recognition: Features versus Templates. IEEE Transactions on Pattern Analysis, 1993.
12)Ross AA, Jain AK, Nandakumar K. Handbook of Multibiometrics. Springer, 2006.
13)Grother P et al. Face Recognition Vendor Test (FRVT). NIST, 2019.
14)Phillips PJ et al. Overview of the Face Recognition Grand Challenge. IEEE CVPR, 2005.
15)W3Schools. Web Development Tutorials. Available: https://www.w3schools.com
16)Grippa VM, Kuzmichev S. Learning MySQL. O’Reilly Media, 2021.
17)Hoang N. Comprehensive Student Management System.
18)Al-Muhaidhri G, Hussain J. Smart Attendance System using Face Recognition. IJERT, 2019.
19)Sharanya T et al. Online Attendance using Facial Recognition. IJER, 2020.
20)Anjara Y et al. Facial Recognition Based Attendance System. IRJET, 2021.
21)Becerra A et al. Portable Student Attendance Management using Biometrics. Multimedia Tools and Applications, 2024.
22)Duckett J. HTML and CSS: Design and Build Websites. Wiley, 2011.
23)Crockford D. JavaScript: The Good Parts. O’Reilly Media, 2008.
How to Cite This Paper
Namrata Pandey, Aditi Srivastava, Anjali Maurya, Sanjana Singh, Mudit Dubey (2026). Smart_Face: A Web-Based Face Identification and Attendance System. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
