Design of Classroom Check-in Algorithm Based on Face Recognition

DOI: https://doi.org/10.62517/jbdc.202401108

Author(s)

Xiaofei Song1,*, Mingju Chen1,2

Affiliation(s)

1College of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin, Sichuan, China 2Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science & Engineering, Yibin, Sichuan, China *Corresponding Author.

Abstract

In order to understand the attendance of students in the classroom quickly and accurately, this paper proposes a classroom face recognition check-in algorithm developed based on Python language and OpenCV library. The Video Capture function provided by OpenCV library is used to call the camera for image acquisition, and then the acquired image is preprocessed to realize face detection using Haar-like features, the LBPH algorithm is used to extract features from the face image, and a training model is obtained through feature training and stored in the database. After the check-in is initiated, the acquired images are preprocessed, face detection and feature extraction are performed, and they are compared with the model previously stored in the database. The experimental results show that the algorithm is able to efficiently and accurately realize classroom face recognition check-in while ensuring lower cost.

Keywords

Open CV; Face Recognition; Face Detection; LBPH; Live Face Recognition

References

[1]Zeng D, Veldhuis R, Spreeuwers L. A survey of face recognition techniques under occlusion. IET biometrics, 2021, 10(6): 581-606. [2]Dong J. Application of Face Recognition Technology based on PCA Algorithm in Campus security Management. Revista Ibérica de Sistemas e Tecnologias de Informação, 2023 (E62): 495-508. [3]Gandhi R, Jain K, Mandliya N, et al. Effective Inexpensive and Robust Solution to Classroom Attendance Recording Using Face Detection Technology. International Journal of Research in Engineering, Science and Management, 2023, 6(5): 53-57. [4]Sunaryono D, Siswantoro J, Anggoro R. An android based course attendance system using face recognition. Journal of King Saud University-Computer and Information Sciences, 2021, 33(3): 304-312. [5]Oloyede, M.O., Hancke, G.P. & Myburgh, H.C. A review on face recognition systems: recent approaches and challenges. Multimed Tools Appl 79, 27891–27922 (2020). [6]Guleria A , Krishan K ,Sharma V , et al.Global adoption of facial recognition technology with special reference to India-Present status and future recommendations..Medicine, science, and the law, 2024, 258024241227717-258024241227717. [7]Chen, Mingju, Zhengxu Duan, Lan Li, Sihang Yi, and Anle Cui. 2023. "A Two-Stage Image Inpainting Technique for Old Photographs Based on Transfer Learning" Electronics 12, no. 15: 3221. [8]Chen, Mingju, Sihang Yi, Zhongxiao Lan, and Zhengxu Duan. 2023. "An Efficient Image Deblurring Network with a Hybrid Architecture" Sensors 23, no. 16: 7260. https://doi.org/10.3390/s23167260 [9]Mingju Chen, Zhongxiao Lan, Zhengxu Duan, Sihang Yi, Qin Su. HDS-YOLOv5: An improved safety harness hook detection algorithm based on YOLOv5s. Mathematical Biosciences and Engineering, 2023, 20(8): 15476-15495. doi: 10.3934/mbe.2023691 [10]Hasan R T H, Sallow A B. Face Detection and Recognition Using OpenCV. Journal of Soft Computing and Data Mining, 2021, 2(2): 86-97. [11]Olagoke S A ,Haidi I ,Siang S T , et al.Custom Face Classification Model for Classroom Using Haar-Like and LBP Features with Their Performance Comparisons.Electronics,2021,10(2):102-102. [12]Haar-features training parameters analysis in boosting based machine learning for improved face detection. International Journal of Advanced Technology and Engineering Exploration (IJATEE),2021,8(80): [13]Yanjing L,Mudassir K ,Dilshad M A .Face recognition algorithm based on stack denoising and self-encoding LBP.Journal of Intelligent Systems,2022,31(1):501-510. [14]Hangzhou Q ,Yinwei W .Application of Optimized Local Binary Pattern Algorithm in Small Pose Face Recognition under Machine Vision.Multimedia Tools and Applications,2022,81(20):29367-29381. [15]Smita K, Shilpa G, Ketan K, et al.Face Liveness Detection Using Artificial Intelligence Techniques: A Systematic Literature Review and Future Directions.Big Data and Cognitive Computing, 2023, 7(1):37-37. [16]Lingxue S ,Changsong L .Face Liveness Detection Based on Joint Analysis of RGB and Near-Infrared Image of Faces.Electronic Imaging,2018,2018(10):373-1.