HAGR-D: A Novel Approach for Gesture Recognition with Depth Maps

The hand is an important part of the body used to express information through gestures, and its movements can be used in dynamic gesture recognition systems based on computer vision with practical applications, such as medical, games and sign language. Although depth sensors have led to great progre...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2015-11, Vol.15 (11), p.28646-28664
Main Authors: Santos, Diego G, Fernandes, Bruno J T, Bezerra, Byron L D
Format: Article
Language:English
Subjects:
Algorithms
CIPBR
Databases, Factual
Datasets
DTW
dynamic gesture
Dynamical systems
Dynamics
Gestures
HCI
HMM
Humans
Image Processing, Computer-Assisted - methods
Invariants
Markov models
Pattern Recognition, Automated - methods
Recognition
Sensors
Sign language
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Summary:The hand is an important part of the body used to express information through gestures, and its movements can be used in dynamic gesture recognition systems based on computer vision with practical applications, such as medical, games and sign language. Although depth sensors have led to great progress in gesture recognition, hand gesture recognition still is an open problem because of its complexity, which is due to the large number of small articulations in a hand. This paper proposes a novel approach for hand gesture recognition with depth maps generated by the Microsoft Kinect Sensor (Microsoft, Redmond, WA, USA) using a variation of the CIPBR (convex invariant position based on RANSAC) algorithm and a hybrid classifier composed of dynamic time warping (DTW) and Hidden Markov models (HMM), called the hybrid approach for gesture recognition with depth maps (HAGR-D). The experiments show that the proposed model overcomes other algorithms presented in the literature in hand gesture recognition tasks, achieving a classification rate of 97.49% in the MSRGesture3D dataset and 98.43% in the RPPDI dynamic gesture dataset.
ISSN:1424-8220
1424-8220
DOI:10.3390/s151128646