Using Deep Learning with Thermal Imaging Camera to Record Employee Attendance System

Main Article Content

Amonpan Chomklin
Nuttareepan Nittayoosakulchot


Today every country in the world faces a COVID-19 situation, making life different from daily life or work life. Therefore, organizations or companies adopt a primary diagnostic method for COVID-19 by having a system to scan an individual facial temperature using a thermal imaging camera to check if that person has initial symptoms of COVID-19 or not. This research focuses on the development of an attendance record system with a thermal imaging camera combination with Deep Learning to optimize the collection and processing of data to classify or identify employees precisely and reduce step to record the working hours of employees. The experiment found an average of face recognition the mean accuracy was 81.85%, and the mean processing time was 0.26 seconds. The research was satisfying when compared the research on the development of a time recording system with face detection and recognition using the Haar-Like Feature technique to detect faces and using the Local Binary Patterns Histogram to recognize faces with accurate of facial recognition at 48%. According to the experiment, the result was highly satisfying in terms of accurate data and processing time. Moreover, the developed system produces accurate and precise information with convenience and safety.

Article Details

Research Article


V. Zoumpourlis, M. Goulielmaki, E. Rizos, S. Baliou, and D. Spandidos, “The COVID-19 pandemic as a scientific and social challenge in the 21st century,” Mol. Med. Rep., vol. 22, no. 4, pp. 3035–3048, Oct. 2020, doi: 10.3892/mmr.2020.11393.

J. Sricham. “The day that Thailand knows COVID-19.” (in Thai), (accessed Sep. 9, 2020).

W. Yawai, K. Sirijinda, and P. Banjongrot, “Mobile application for time attendance system using facial recognition,” J. Project Comp. Sci. Inf. Technol., vol. 4, no. 1, pp. 29–36, 2018.

T. Ahonen, A. Hadid, and M. Pietikainen, “Face description with local binary patterns: Application to face recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 12, pp. 2037–2041, 2006, doi: 10.1109/TPAMI.2006.244.

P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. and Pattern Recognit., Kauai, HI, USA, 2001, pp. I-511–I-518, doi: 10.1109/CVPR.2001.990517.

D. Sheel. “What is Deep Learning?.” (accessed Sep. 11, 2020).

S. Chintalapati and M. V. Raghunadh, “Automated attendance management system based on face recognition algorithms,” in IEEE Int. Conf. Comp. Intell. and Comp. Res., Enathi, India, Dec. 2013, pp. 1–5, doi: 10.1109/ICCIC.2013.6724266.

J. Sanuksan and O. Surinta, “Deep convolutional neural networks for plant recognition in natural environment,” (in Thai), J. Sci. Technol. Mahasarakham Univ., vol. 38, no. 2, pp. 113–124, 2019.

K. Treeprapin, P. Na-udom, and P. Kongchai, “Developing a system to verify student enrollment with facial recognition method,” (in Thai), J. Sci. Technol., Ubon Ratchathani Univ., vol. 20, no. 2, pp. 92–105, 2018.

P. Jaturawat, P. Pongmanawut, and M. Phankokkruad, “Development of a learning record system with face detection and recognition,” (in Thai), KMITL J. Inf. Technol., vol. 5, no. 1, pp. 1–11, 2017.