_____________________________________________________________________________________________________ *Corresponding author: E-mail: sopokuoppong@yahoo.com; Asian Journal of Research in Computer Science 3(1): 1-14, 2019; Article no.AJRCOS.47899 ISSN: 2581-8260 Human Fatigue Characterization and Detection Using the Eyelid State and Kalman Filter Dominic Asamoah 1 , Emmanuel Ofori Oppong 1 , Peter Amoako-Yirenkyi 2 and Stephen Opoku Oppong 3* 1 Department of Computer Science, KNUST, Ghana. 2 Department of Mathematics, KNUST, Ghana. 3 Faculty of Computing and Information Systems, GTUC, Ghana. Authors’ contributions This work was carried out in collaboration among all authors. All authors read and approved the final manuscript. Article Information DOI: 10.9734/AJRCOS/2019/v3i130082 Editor(s): (1) Dr. Omidiora, Elijah Olusayo, Professor, Department of Computer Science and Engineering, Faculty of Engineering and Technology, Ladoke Akintola University of Technology (LAUTECH), Nigeria. Reviewers: (1) Zlatin Zlatev, Trakia University, Bulgaria. (2) Anthony Spiteri Staines, University of Malta, Malta. Complete Peer review History: http://www.sdiarticle3.com/review-history/47899 Received 25 December 2018 Accepted 05 March 2019 Published 15 March 2019 ABSTRACT One of the most promising commercial applications of Human Computer Interface is the vision based Human fatigue detection systems. Most methods and algorithms currently rely heavily on movement of the head and the colorization of the eye ball. In this paper, a new algorithm for detecting human fatigue by relying primarily on eyelid movements as a facial feature is proposed. The features of the eye region and eyelid movement which are geometric in nature are processed alongside each other to determine the level of fatigue of a person. Haar classifiers are employed to detect the eye region and eyelid features. The eye region is, however processed to ascertain attributes of eyelid movement of each individual of interest. The eyelids are then detected as either opened, closed or in transition state. The movement or velocity of the eyelid is tracked using a Kalman filtered velocity function. This algorithm calculates a human blink cycle for each individual, and estimates the associated errors of the eye movement due to friction using the Kalman filter. Original Research Article