Eye Blinking for Command Generation Based on Deep Learning

Abstract

Due to progress in the field of deep learning in order to find and track objects through the use of computer vision in the service of large segments of the population, as it was adopted in the field of serving people b with special needs for the sake of dialogue and implementation of many requests in this research, a series of commands for use by people with special needs with speech problems or paralysis, where the ability to use the eye blink is very useful for social communication, were developed. In this research, the orders needed by the target people with special needs were studied, and (11) commands were identified that can be increased according to the intended sample. A table of commands was built depending on the length of the eye blink period. By creating a modified CNN: Convolutional Neural Network structure and training it on 2 different database, deep learning was used to identify and determine if the eye is closed or open (mrlEye2018 and Closed Eye in the Wlid:CEW).It was then followed by a test on all of the chosen examples, in different contexts and at various ages. The test on the data yielded excellent results, with 99% percent accuracy on data from test samples and 97.5% and 96% percent accuracy on training data in each case. To check the cases (11) suggested commands, the proposed system was evaluated on a collection of videos taken in real time and under normal recording circumstances through a camera, and the correctness of generating the codes that was proposed was 94% percent on four tries in each of the above instances.