| Abstrak/Abstract |
Two years on with Covid-19, touchless technology
has evolved from a device that symbolizes luxury to some-
thing that is necessary. Eye tracker is one type of touchless
technologies that uses user’s gaze to interact with computer
without touching the screen. Development of spontaneous gaze-
based interaction is progressing very rapidly. Researchers have
developed various object selection methods without prior gaze-
to-screen calibration. Recently, the conventional approach of
setting threshold was developed as a gaze-based object selection
method. However, the use of threshold values is considered
non-adaptive and requires additional data pre-processing to
handle noises. To overcome this problem, deep learning is
used as an object selection method for spontaneous gaze-based
interaction. Deep learning does not require any data pre-
processing method to achieve accurate object selection results.
Out of five deep learning algorithms that were evaluated, LSTM
(Long Short-Term Memory) and BiLSTM (Bidirectional Long
Short-Term Memory) networks achieved comparable accuracy
of 95.17±0.95% and 95.15±1.17%, respectively. In future,
our research is promising for development of real-time object
selection technique for touchless public display. |
