TY - GEN
T1 - The Impact of Keypoints Normalization on SIBI Recognition using Modified Shift-GCN
AU - Adhiyaksa, Fariz Ardin
AU - Suciati, Nanik
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Sign language is a form of non-verbal communication usually used by someone who is speech impaired or deaf to communicate with others. There have been many efforts to develop sign language recognition. However, the dataset is mainly limited to images containing the hand area with static poses. In a previous study, Modified Shift-Graph Convolutional Network (Modified Shift-GCN) performed well on American Sign Language Recognition (ASL) with a relatively simple graph dataset without tracking and normalization. In this study, we explore the implementation of Modified Shift-GCN to recognize the Indonesian Sign Language (SIBI) on a more complex graph dataset combining the hand and body graphs with dynamic poses. We investigate the effect of key-point tracking and normalization on the performance of the Modified Shift-GCN on sign language recognition. The experiments using 10-folds cross-validation show that the normalization yield an average accuracy of 97.9%, superior to that without normalization with an average accuracy of 55.3%.
AB - Sign language is a form of non-verbal communication usually used by someone who is speech impaired or deaf to communicate with others. There have been many efforts to develop sign language recognition. However, the dataset is mainly limited to images containing the hand area with static poses. In a previous study, Modified Shift-Graph Convolutional Network (Modified Shift-GCN) performed well on American Sign Language Recognition (ASL) with a relatively simple graph dataset without tracking and normalization. In this study, we explore the implementation of Modified Shift-GCN to recognize the Indonesian Sign Language (SIBI) on a more complex graph dataset combining the hand and body graphs with dynamic poses. We investigate the effect of key-point tracking and normalization on the performance of the Modified Shift-GCN on sign language recognition. The experiments using 10-folds cross-validation show that the normalization yield an average accuracy of 97.9%, superior to that without normalization with an average accuracy of 55.3%.
KW - SIBI
KW - image recognition
KW - keypoints normalization
KW - pattern recognition
KW - sign language recognition
UR - http://www.scopus.com/inward/record.url?scp=85150468371&partnerID=8YFLogxK
U2 - 10.1109/ICITISEE57756.2022.10057716
DO - 10.1109/ICITISEE57756.2022.10057716
M3 - Conference contribution
AN - SCOPUS:85150468371
T3 - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022
SP - 402
EP - 407
BT - Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2022
Y2 - 13 December 2022 through 14 December 2022
ER -
