TY - GEN
T1 - Skeleton based dynamic hand gesture recognition using LSTM and CNN
AU - Ikram, Aamrah
AU - Liu, Yue
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/8/5
Y1 - 2020/8/5
N2 - Dynamic Hand Gestures offer a natural, non-verbal way of communication that can substitute other communication modalities like verbal speech and script writing. Not only for the voice command, hand gestures also play significant role in Augmented Reality (AR), Virtual Reality (VR) and games. There are some factors like computational cost, flexibility and recognition accuracy that can impact the incorporation of hand gestures in these fields. In this paper, a Dynamic Hand Gesture Recognition (DHGR) approach is propose that is based on Convolutional Neural Network (CNN) and long-short term memory (LSTM). This system is trained to execute the sequence of 3D input data along with the velocities and positions information learned from Leap Motion Controller (LMC).When evaluated and compared with state-of-art DHGR methods, this architecture shows relative high accuracy of 98%.
AB - Dynamic Hand Gestures offer a natural, non-verbal way of communication that can substitute other communication modalities like verbal speech and script writing. Not only for the voice command, hand gestures also play significant role in Augmented Reality (AR), Virtual Reality (VR) and games. There are some factors like computational cost, flexibility and recognition accuracy that can impact the incorporation of hand gestures in these fields. In this paper, a Dynamic Hand Gesture Recognition (DHGR) approach is propose that is based on Convolutional Neural Network (CNN) and long-short term memory (LSTM). This system is trained to execute the sequence of 3D input data along with the velocities and positions information learned from Leap Motion Controller (LMC).When evaluated and compared with state-of-art DHGR methods, this architecture shows relative high accuracy of 98%.
KW - Convolutional Neural Network (CNN)
KW - Dynamic Hand Gestures Recognition (DHGR)
KW - Leap Motion Controller (LMC)
UR - http://www.scopus.com/inward/record.url?scp=85097346874&partnerID=8YFLogxK
U2 - 10.1145/3421558.3421568
DO - 10.1145/3421558.3421568
M3 - Conference contribution
AN - SCOPUS:85097346874
T3 - ACM International Conference Proceeding Series
SP - 63
EP - 68
BT - Proceedings of 2020 2nd International Conference on Image Processing and Machine Vision, IPMV 2020 and International Conference on Pattern Recognition and Machine Learning
PB - Association for Computing Machinery
T2 - 2nd International Conference on Image Processing and Machine Vision, IPMV 2020 and International Conference on Pattern Recognition and Machine Learning
Y2 - 5 August 2020 through 7 August 2020
ER -