Main Article Content



Rationale: Research on the correlation between brain activity and blinking is very important in finding a way to improve non-invasive diagnostic methods for brain diseases. Parkinson’s disease has a 29% misdiagnose rate, which becomes even higher during the early stages of the disease. As a result, a nonintrusive way of measuring brain activity is needed. Among several different biometrics, the eye blinking rate was recognized to have correlation with complex activities in the brain.

Procedures: This experiment measures the blinking rate of adolescents, who are maturing their prefrontal cortex using algorithm based on dlib and openCV library. Eye blinking was measured during the playing of a game, where various decisions must be made in a short amount of time. Through this measurement, the number of blinks in non-competitive and competitive situations was compared, and determined which situation results in faster blinking through the Eye Aspect Ratio (EAR). In addition, it could be examined on which objective affects brain activity the most, by measuring the number of blinks and EAR in different genres of games.

Results: The optimal threshold value for the blinker program was 0.18. The range of the slope of the real-time recording of the blinking counter is about 4 times larger in the competitive mode. These results support eye blink count and EAR varied in all games in the competitive mode. This suggests that each situation in the competitive games was unique and required different amounts of brain activity. It was inferred that the brain worked actively in these stressful situations.

AI algorithm, blinking detection, blinking pathology, competitive games, game theory

Article Details

How to Cite
JEONG, C., & KIM, T. (2021). EYE BLINK DETECTION USING ALGORITHM BASED ON dlib AND openCV LIBRARY FOR GAME PLAYERS IN COMPETITIVE ENVIRONMENTS. Journal of International Research in Medical and Pharmaceutical Sciences, 16(2), 33-45. Retrieved from https://www.ikprress.org/index.php/JIRMEPS/article/view/6551
Original Research Article


Jankovic J, Havins WE, Wilkins RB. Blinking and blepharospasm: Mechanism, diagnosis, and management. Jama. 1982;248(23):3160-3164.

Zhang Q, Strangman GE, Ganis G. Adaptive filtering to reduce global interference in non-invasive NIRS measures of brain activation: How well and when does it work?. Neuroimage. 2009;45(3):788-794.

Askamp J, van Putten MJ. Mobile EEG in epilepsy. International Journal of Psychophysiology. 2014;91(1):30-35.

López-Gil JM, Virgili-Gomá J, Gil R, Guilera T, Batalla I, Soler-González J, García R. Method for improving EEG based emotion recognition by combining it with synchronized biometric and eye tracking technologies in a non-invasive and low cost way. Frontiers in Computational Neuroscience. 2016;10:85.

Berka C, Levendowski DJ, Ramsey CK, Davis G, Lumicao MN, Stanney K, Stibler K. Evaluation of an EEG workload model in an Aegis simulation environment. In Biomonitoring for Physiological and Cognitive Performance during Military Operations. International Society for Optics and Photonics. 2005;5797:90-99.

Derick LR, Gabriel GS, Máximo LS, Olivia FD, Noé CS, Juan OR. Study of the user's eye tracking to analyze the blinking behavior while playing a video game to identify cognitive load levels. In 2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). IEEE. 2020;4:1-5.

Provine RR, Cabrera MO, Brocato NW, Krosnowski KA. When the whites of the eyes are red: A uniquely human cue. Ethology. 2011;117(5):395-399.

Grauman K, Betke M, Gips J, Bradski GR. Communication via eye blinks-detection and duration analysis in real time. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001. IEEE. 2001;1:I-I.

Bhoyar MAM, Sawalkar SN. Implementation on visual analysis of eye state using image processing for driver fatigue detection. International Research Journal of Engineering and Technology (IRJET). 2019;6(4):4340-4346.

Mohammed MA, Abd Ghani MK, Arunkumar NA, Hamed RI, Abdullah MK, Burhanuddin MA. A real time computer aided object detection of nasopharyngeal carcinoma using genetic algorithm and artificial neural network based on Haar feature fear. Future Generation Computer Systems. 2018;89:539-547.

Soukupova T, Cech J. Eye blink detection using facial landmarks. In 21st computer vision winter workshop, Rimske Toplice, Slovenia; 2016.

Peng S, Chen L, Gao C, Tong RJ. Predicting students' attention level with interpretable facial and head dynamic features in an online tutoring system (Student Abstract). In Proceedings of the AAAI Conference on Artificial Intelligence. 2020;34(10):13895-13896.

Vignesh CP, Sriram R. Eye blink controlled virtual interface using Opencv and Dlib. European Journal of Molecular & Clinical Medicine 2020;7(8):2119- 2126.

Granic I, Lobel A, Engels RC. The benefits of playing video games. American Psychologist. 2014;69(1):66.