Vision Based Hand Gesture Recognition Using 3D Shape Context

Hand gesture recognition is a popular topic in computer vision and makes human-computer interaction more flexible and convenient. The representation of hand gestures is critical for recognition. In this paper, we propose a new method to measure the similarity between hand gestures and exploit it for...

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Bibliographic Details
Published in:IEEE/CAA journal of automatica sinica 2021-09, Vol.8 (9), p.1600-1613
Main Authors: Zhu, Chen, Yang, Jianyu, Shao, Zhanpeng, Liu, Chunping
Format: Article
Language:English
Subjects:
3D shape context
Algorithms
Computer vision
Context
Data mining
depth map
Feature extraction
Gesture recognition
hand gesture recognition
hand shape segmentation
Heuristic algorithms
human-computer interaction
Representations
Sensors
Shape
Shape recognition
Three-dimensional displays
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Summary:Hand gesture recognition is a popular topic in computer vision and makes human-computer interaction more flexible and convenient. The representation of hand gestures is critical for recognition. In this paper, we propose a new method to measure the similarity between hand gestures and exploit it for hand gesture recognition. The depth maps of hand gestures captured via the Kinect sensors are used in our method, where the 3D hand shapes can be segmented from the cluttered backgrounds. To extract the pattern of salient 3D shape features, we propose a new descriptor-3D Shape Context, for 3D hand gesture representation. The 3D Shape Context information of each 3D point is obtained in multiple scales because both local shape context and global shape distribution are necessary for recognition. The description of all the 3D points constructs the hand gesture representation, and hand gesture recognition is explored via dynamic time warping algorithm. Extensive experiments are conducted on multiple benchmark datasets. The experimental results verify that the proposed method is robust to noise, articulated variations, and rigid transformations. Our method outperforms state-of-the-art methods in the comparisons of accuracy and efficiency.
ISSN:2329-9266
2329-9274
DOI:10.1109/JAS.2019.1911534