2 research outputs found
Hand Gesture Recognition Based on Keypoint Vector
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
An Exploration into Human–Computer Interaction::Hand Gesture Recognition Management in a Challenging Environment
Scientists are developing hand gesture recognition systems to improve authentic, efficient, and effortless human–computer interactions without additional gadgets, particularly for the speech-impaired community, which relies on hand gestures as their only mode of communication. Unfortunately, the speech-impaired community has been underrepresented in the majority of human–computer interaction research, such as natural language processing and other automation fields, which makes it more difficult for them to interact with systems and people through these advanced systems. This system’s algorithm is in two phases. The first step is the Region of Interest Segmentation, based on the color space segmentation technique, with a pre-set color range that will remove pixels (hand) of the region of interest from the background (pixels not in the desired area of interest). The system’s second phase is inputting the segmented images into a Convolutional Neural Network (CNN) model for image categorization. For image training, we utilized the Python Keras package. The system proved the need for image segmentation in hand gesture recognition. The performance of the optimal model is 58 percent which is about 10 percent higher than the accuracy obtained without image segmentation