Affiliation:
1. King Mongkut’s University of Technology North Bangkok Thailand
Abstract
Face recognition technology is widely used in applications. But in some activities it may be too difficult to install the device and the registration boot. That requires both manpower and time, such as enrolling students to attend university activities. If you will use the face scanning system, one by one will waste a lot of time. The other method. It may be easy to falsify. Using digital imagery in student participation to solve problems by developing a system that can detect participants' faces in digital photographs obtained by taking still images and videos from several photographers. And collecting detailed pictures and videos throughout the event it is a digital proof to find the participants to verify their faces match with any student in the database. Who participate in that activity, the system will have Finding and comparing data of pre-recorded students' photographs and the algorithm would checks for duplicate data and records the activity in the database. Where users can specify category or activity name for later inspection
Publisher
North Atlantic University Union (NAUN)
Reference24 articles.
1. H. Zhang, J. Yang, J. Qian and W. Luo, "Nonconvex relaxation based matrix regression for face recognition with structural noise and mixed noise," Neurocomputing, vol. 269, pp. 188-198, 2017.
2. J. Wang, T. Li, Y. Q. Shi, S. Lian and J. Ye, "Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics," Multimedia Tools and Applications, vol. 76, no. 22, pp. 23721-23737, 2017.
3. A. Velicanu, I. Lungu, V. Diaconita and C. Nisioiu, "The 9 th International Scientific Conference eLearning and software for Education," pp. 380-386, 2013.
4. S. Soltanpour, B. Boufama and Q. M. Jonathan Wu, "A survey of local feature methods for 3D face recognition," Pattern Recognition, vol. 72, pp. 391-406, 2017.
5. K. Shang, Z. H. Huang, W. Liu and Z. M. Li, "A single gallery-based face recognition using extended joint sparse representation," Applied Mathematics and Computation, vol. 320, pp. 99-115, 2018.