Performance evaluation of image processing algorithms for eye blinking detection

• Published in: Measurement : journal of the International Measurement Confederation Vol. 223; p. 113767
• Main Authors: Attivissimo, Filippo, D'Alessandro, Vito Ivano, Di Nisio, Attilio, Scarcelli, Giuliano, Schumacher, Justin, Lanzolla, Anna Maria Lucia
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2023
• Subjects: Computer vision; Eye blinking; Image processing; Ophthalmology; RGB camera; Computer vision; Ophthalmology; Image processing; Eye blinking; RGB camera
• ISSN: 0263-2241
• DOI: 10.1016/j.measurement.2023.113767

•Performance analysis of different video-based eye blinking detection.•Development of a low-cost imaging acquisition system.•Quality metrics analysis of proposed algorithms.•Performance comparison between RGB and Grayscale images.•The red channel of the RGB camera achieves in our experiments a 100 % eye blink detection rate for event detection. The eye is considered a rich source for gathering information on our daily lives. In addition, the necessity to classify the eye as open or closed and detect eye blinks is increasing in various fields. For instance, it has been proven that eye blinks can be related to different ophthalmological conditions. Nevertheless, accurate, and robust blink estimation poses significant challenges to the efficacy of real-time applications due to the variability of eye images and light conditions. In this paper eye blinking has been monitored using a chin rest and an iOS smartphone attached to a cartesian machinery. Five video-based eye blink algorithms to classify frames have been analyzed and compared in terms of performance. To evaluate the results, different widely used statistical metrics have been employed. The results show that the proposed techniques successfully detect eye blinks, whereas the frame classifier shows different scores depending on the algorithm used.