Abstract
The eyes are the most important feature of our bodies because they allow us to see and explore the world. Nowadays, technology is continually evolving, paving the way for greater development and increased use of gadgets by everyone. When people stare at digital screens for long periods of time, they develop eye strain and visual issues, which is known as computer vision syndrome (CVS). The best way to avoid visual problems caused by digital screens is to take appropriate preventive measures such as getting regular eye care. To protect users from eye disorders, we created a model that uses the Viola–Jones method and the SVM classifier to estimate the user's eye blinking ratio. As a result, the proposed approach calculates locations of significance and another scalar parameter is derived—ratio of the eyes (EAR)—that characterizes each frame's eye opening. Finally, in a limited temporal window, eye blinks are recognized as a pattern of EAR values using an SVM classifier. The user can be notified about his gadget usage based on the results of the eye blink ratio and gradually diminish his addiction to digital screens that affect his eyes.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Adireddi, V.S. et al. (2023). Detection of Eye Blink Using SVM Classifier. In: Ogudo, K.A., Saha, S.K., Bhattacharyya, D. (eds) Smart Technologies in Data Science and Communication. Lecture Notes in Networks and Systems, vol 558. Springer, Singapore. https://doi.org/10.1007/978-981-19-6880-8_18
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DOI: https://doi.org/10.1007/978-981-19-6880-8_18
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