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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM61827
Volume:
10 |
4 |
April 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
INFORMATION TECHNOLOGY
DG RUPAREL COLLEGE
This paper presents a multimodal biometric attendance system that integrates face recognition and voice authentication to automate attendance management in academic environments. Traditional attendance methods are often inefficient and vulnerable to manipulation such as proxy attendance. The proposed system utilizes facial feature extraction and speaker embedding techniques to ensure accurate identification of students. A role-based architecture is implemented, allowing teachers to manage subjects and record attendance, while students authenticate using biometric inputs. The system is supported by a cloud-based backend for real-time data storage and retrieval. Experimental results indicate improved accuracy, reduced manual effort, and enhanced security compared to conventional methods. The integration of multiple biometric modalities increases system robustness and reliability [1][4].
KEYWORDS
This paper presented a multimodal biometric attendance system using face and voice recognition for automated attendance management. The system reduces manual effort, improves accuracy, and prevents proxy attendance by using biometric authentication [1][4]. The integration of a cloud-based backend ensures real-time data storage and easy access for teachers and students [8]. Future improvements may include the use of deep learning models for higher accuracy [5], deployment as a mobile application, addition of more biometric modalities such as fingerprint recognition, and enhancement of system performance in noisy and low-light environments [2][3].
[1] A. K. Jain, A. Ross, and S. Prabhakar, “An Introduction to Biometric Recognition,” IEEE Transactions on Circuits and Systems for Video Technology, 2004. [2] M. Turk and A. Pentland, “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience, 1991. [3] D. A. Reynolds, “An Overview of Automatic Speaker Recognition Technology,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2002. [4] R. Brunelli and T. Poggio, “Face Recognition: Features versus Templates,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993. [5] F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. [6] dlib Documentation, Davis E. King, 2009. [7] Resemblyzer Documentation, 2019. [8] Supabase Official Documentation, 2020–present.
Publication Date
4/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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