期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：4
DOI：10.14569/IJACSA.2020.0110496
出版社：Science and Information Society (SAI)
摘要：Hand gestures with finger relationships are among the toughest features to extract for machine recognition. In this paper, this particular research challenge is addressed with 3D hand joint features extracted from distance measurements which are then colour mapped as spatio temporal features. Further patterns are learned using an 8-layer convolutional neural network (CNN) to estimate the hand gesture. The results showed a higher degree of recognition accuracy when compared to similar 3D hand gesture methods. The recognition accuracy for our dataset KL 3DHG with 220 classes was around 94.32%. Robustness of the proposed method was validated with only available benchmark 3D skeletal hand gesture dataset DGH 14/28.
关键词：Gesture recognition; 3D motion capture; deep learn-ing; joint relational distance maps
