TY - GEN
T1 - Hand Gesture Recognition Based on Keypoint Vector
AU - Arwoko, Heru
AU - Yuniarno, Eko Mulyanto
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Deep Neural Network
KW - Hand Gesture Recognition
KW - Keypoint
KW - Normalized Vector
UR - http://www.scopus.com/inward/record.url?scp=85139632489&partnerID=8YFLogxK
U2 - 10.1109/IES55876.2022.9888333
DO - 10.1109/IES55876.2022.9888333
M3 - Conference contribution
AN - SCOPUS:85139632489
T3 - IES 2022 - 2022 International Electronics Symposium: Energy Development for Climate Change Solution and Clean Energy Transition, Proceeding
SP - 530
EP - 533
BT - IES 2022 - 2022 International Electronics Symposium
A2 - Yunanto, Andhik Ampuh
A2 - Prayogi, Yanuar Risah
A2 - Putra, Putu Agus Mahadi
A2 - Hermawan, Hendhi
A2 - Nailussa'ada, Nailussa'ada
A2 - Ruswiansari, Maretha
A2 - Ridwan, Mohamad
A2 - Gamar, Farida
A2 - Ramadhani, Afifah Dwi
A2 - Rahmawati, Weny Mistarika
A2 - Rusli, Muhammad Rizani
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th International Electronics Symposium, IES 2022
Y2 - 9 August 2022 through 11 August 2022
ER -
