RGB-D feature extraction method for hand gesture recognition based on a new fast and accurate multi-channel cartesian Jacobi moment invariants

Ouariachi, Ilham El; Benouini, Rachid; Zenkouar, Khalid; Zarghili, Arsalane; Fadili, Hakim El

doi:10.1007/s11042-022-12161-2

Cited by 7 publications

(1 citation statement)

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“…A new set of image moment invariant-based (RGB-D) feature extraction methods is proposed. Geometric moment invariants under rotation, scaling and translation transformations are used to express multichannel Cartesian Jacobi moments [32]. A novel quaternion-based multi-objective loss function is designed.…”

Section: Related Workmentioning

confidence: 99%

Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D images

Sun

Weng

Luo

et al. 2022

IET Image Processing

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With the rapid development of sensor technology and artificial intelligence, the video gesture recognition technology under the background of big data makes human-computer interaction more natural and flexible, bringing richer interactive experience to teaching, on-board control, electronic games, etc. In order to perform robust recognition under the conditions of illumination change, background clutter, rapid movement, partial occlusion, an algorithm based on multi-level feature fusion of two-stream convolutional neural network is proposed, which includes three main steps. Firstly, the Kinect sensor obtains RGB-D images to establish a gesture database. At the same time, data enhancement is performed on training and test sets. Then, a model of multi-level feature fusion of twostream convolutional neural network is established and trained. Experiments result show that the proposed network model can robustly track and recognize gestures, and compared with the single-channel model, the average detection accuracy is improved by 1.08%, and mean average precision (mAP) is improved by 3.56%. The average recognition rate of gestures under occlusion and different light intensity was 93.98%. Finally, in the ASL dataset, LaRED dataset, and 1-miohand dataset, recognition accuracy shows satisfactory performances compared to the other method.

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Section: Related Workmentioning

confidence: 99%