Recognition of Eye-Written Characters with Limited Number of Training Data Based on a Siamese Network

Abstract

Developing a hum–computer interface (HCI) is essential, especially for those that have spinal cord injuries or paralysis, because of the difficulties associated with the application of conventional devices and systems. Eye-writing is an HCI that uses eye movements for writing characters such that the gaze movements form letters. In addition, it is a promising HCI because it can be utilized even when voices and hands are inaccessible. However, eye-writing HCI has low accuracy and encounters difficulties in obtaining data. This study proposes a method for recognizing eye-written characters accurately and with limited data. The proposed method is constructed using a Siamese network, an attention mechanism, and an ensemble algorithm. In the experiment, the proposed method successfully classified the eye-written characters (Arabic numbers) with high accuracy (92.78%) when the ratio of training to test data was 2:1. In addition, the method was tested as the ratio changed, and 80.80% accuracy was achieved when the number of training data was solely one-tenth of the test data.