Human-computer interaction (HCI) is usually
associated with using popular input devices such as a mouse or
keyboard. In other cases hand gestures can actually be useful
for human-computer interaction when hand gestures are
needed to make the game controls more interesting. There are
three basic controls as input mouse: move, click, and drag.
Hand gestures and hand shape are different for each person.
This becomes a problem during automatic recognition. Recent
research has proven the success of the Deep Neural Network
(DNN) for representation and high accuracy in hand gesture
recognition. DNN algorithms can study complex and nonlinear
relationships between features by applying multiple
layers. This paper proposes hand feature based on the
normalized keypoint vector using DNN. The model was trained
on 2250 hand datasets which were divided into 3 classes to
identify the mouse movement. The network design uses
multilayer with neuron sizes (13, 12, 15, 14) with 500 epochs
and achieves the best accuracy of 98.5% for normalized
features. The important work in this research is the use of
keypoint vector from hand gestures as features to be fed to the
DNN to achieve good accuracy
