2020
DOI: 10.1007/s10044-020-00866-9
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Abstract: Hand gesture recognition is a challenging task due to the complexity of hand movements and to the variety among the same gesture performed by distinct subjects. Recent technologies, such as Kinect sensor, provide new opportunities, allowing to capture both RGB and depth (RGB-D) images, which offer high discriminant information for efficient hand gesture recognition. In the aspect of feature extraction, the traditional methods process the RGB and depth information independently. In this paper, we propose a robu… Show more

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Cited by 15 publications
(2 citation statements)
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“…Both the feature vector and the neural network were tuned by a multi-objective evolutionary algorithm. Elouariachi et al 24 proposed a quaternion Tchebichef moment invariants by using the quaternion algebra for extracting the gesture features. Based on the algebraic properties of the discrete Tchebichef polynomials, the direct derivation of invariants from their orthogonal moments has the robustness against geometrical distortion, noisy conditions and complex background.…”
Section: Related Workmentioning
confidence: 99%
“…Both the feature vector and the neural network were tuned by a multi-objective evolutionary algorithm. Elouariachi et al 24 proposed a quaternion Tchebichef moment invariants by using the quaternion algebra for extracting the gesture features. Based on the algebraic properties of the discrete Tchebichef polynomials, the direct derivation of invariants from their orthogonal moments has the robustness against geometrical distortion, noisy conditions and complex background.…”
Section: Related Workmentioning
confidence: 99%
“…The majority of works concerning gesture and SL recognition uses cameras or other external devices for data acquisition (e.g. [2,3,9,10,12,18,20,22,23,[38][39][40][41]). The main advantage of this solution is the acquisition of all data required for a perfect SL translation, such as position, configuration and movement of the hands, facial expressions, position and movement of the body.…”
Section: State Of the Artmentioning
confidence: 99%