期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 21, 期 1, 页码 234-245出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2018.2856094
关键词
Hand gesture recognition; sign language; semaphoric gestures; Leap Motion Controller (LMC); Recurrent Neural Network (RNN); Long Short Term Memory (LSTM)
资金
- MIUR under grant Departments of Excellence 2018-2022 of the Department of Computer Science of Sapienza University
Hand gesture recognition is still a topic of great interest for the computer vision community. In particular, sign language and semaphoric hand gestures are two foremost areas of interest due to their importance in human-human communication and human-computer interaction, respectively. Any hand gesture can be represented by sets of feature vectors that change over time. Recurrent neural networks (RNNs) are suited to analyze this type of set thanks to their ability to model the long-term contextual information of temporal sequences. In this paper, an RNN is trained by using as features the angles formed by the finger bones of the human hands. The selected features, acquired by a leap motion controller sensor, are chosen because the majority of human hand gestures produce joint movements that generate truly characteristic corners. The proposed method, including the effectiveness of the selected angles, was initially tested by creating a very challenging dataset composed by a large number of gestures defined by the American sign language. On the latter, an accuracy of over 96% was achieved. Afterwards, by using the Shape Retrieval Contest (SHREC) dataset, a wide collection of semaphoric hand gestures, the method was also proven to outperform in accuracy competing approaches of the current literature.
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