Face Registration and Student’s Class Attending Monitoring System using Image Processing with Library Face Recognition
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Abstract
This research present face registration and student’s class attending monitoring system using image processing with library face recognition. It will be useful to check-in all the student’s class attending and also to use the modern technology to register for classroom attendance. The effectiveness of the student's face recognition system was divided into two parts. In the term of comparison between the students’ face images with the database image. Then these image were pass through to Pre-Processing to create a database of student's faces and was compared with those database. In the term using the real-time face images (registration images), the registration images were created using the face recognition image system. The experiment results shown that, in the case of random 100 face image per a student gave the best recognition performance accuracy as 92%. Also, this designed system can view historical record data and transfer it to be document file.
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References
Suksukont A., “Face Detection and Objects on Eyes Boundary using Color Model with Image Processing,” Rajamangala University of Technology Tawan-ok Research Journal, Vol.14, No.1, pp. 42-53, 2021.
Triprapin K., Naudom P. and Kongchai P., “Attendance Monitoring System with Face Recognition Technologies,” Science and Technology Journal, Vol.20, No.2, pp. 92–105, 2018.
Suherwin, Zainuddin Z., Ilham A.A., “The Performance of Face Recognition Using the Combination of Viola- Jones,” Local Binary Pattern Histogram and Euclidean Distance, International Conference on Informatics and Computational Sciences, pp. 1-4, 2020.
Jaturawatthana P., Phongmanawut P. and Phankokkruad M., Development of A Learning Record System with Face Detection and Recognition, Lat Krabang Information Technology Journal, Vol.15, No.1, pp. 1- 11, 2017.
Deng W., Hu J. and Guo J., “Compressive Binary Patterns: Designing a Robust Binary Face Descriptor with Random-Field Eigenfilters,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(3) pp. 758-767, 2019.
Shatnawi Y., Alsmirat M. and Al-Ayyoub M., “Face Recognition using Eigen-Faces and Extension Neural Network,” International Conference on Computer Systems and Applications, pp. 1-7, 2019.
Xiao J., Li S. and Xu Q., “Video-Based Evidence Analysis and Extraction in Digital Forensic Investigation,” Deep Learning: Security and Forensics Research Advances and Challenges Vol.7, pp. 5432-5442, 2019.
Shahbaz A. and Jo K.H., “Moving Object Detection based on Deep Atrous Spatial Features for Moving Camera,” International Symposium on Industrial Electronics, pp. 67-70, 2021.
Kushal M., Kushal Kumar B.V.,Charan Kumar M.J. and Pappa M., “ID Card Detection with Facial Recognition using Tensorflow and OpenCV,” International Conference on Inventive Research in Computing Applications, pp. 742-746, 2020.
Noble F.K.., “Comparison of OpenCV’s Feature Detectors and Feature Matchers,” Proceeding of International Conference on Mechatronics and Machine Vision in Practice, pp. 1-6, 2016.