Comparing Accuracy and Time Complexity of Machine Learning Algorithms for Eye Gesture Recognition

Abstract

The eye motion data can be utilized to perform behavior analysis and improve common applications, such as accessible HCI, interactive interface, marketing, and remote-controlling. This research project compares accuracy and time complexity of three commonly used machine learning algorithms for eye gesture recognition. The importance of this project is to examine ways to improve efficiency in recognizing eye gestures. It was found that the template matching algorithm has the best accuracy, followed by the Pearson correlation algorithm, and lastly the decision tree algorithm. For time performance, it was found that the decision tree algorithm performs the best, closely followed by the Pearson correlation algorithm, and lastly the template matching algorithm. The template matching algorithm is recommended to be used in accuracy-sensitive situations. The decision tree algorithm and the Pearson correlation algorithm are recommended for time-sensitive situations. The algorithms perform better when the directions and other relative properties of input gestures are majorly different. One should consider the properties of the input gesture and the nature of application when it comes to deciding which algorithm to use.