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Multiview Hand Gesture Recognition From Generated Gestures Using Conditional Adversarial Network
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AI
DOI：10.1109/LSENS.2025.3643315.png)
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Abstract
En 中文
Hand gesture recognition systems often struggles when a gesture is occluded by the hand itself or by the other hand. To address this problem, multiview gestures may be used while training a gesture recognition system. Unfortunately, there are very few datasets available that contains different views of every single gesture. In this letter, we propose a method to handle occlusions in single-view sign gestures by generating multiple views from a single-view gesture using conditional multiview gesture synthesis. The generated views help to solve the occlusion problem, thereby enhancing the recognition performance. In addition, we introduce a vision-based multiview hand gesture recognition framework that utilizes the generated multiview gestures for gesture recognition. Experiments conducted on the HGM-4 dataset demonstrate that the generated images are of high quality, photorealistic, and significantly improve the recognition accuracy compared to some other existing methods.
Keywords:
Hands
Gesture recognition
Training
Image color analysis
Generators
Generative adversarial networks
Accuracy
Shape
Convolution
Indexes
Sensor applications
convolutional neural network
hand occlusion
multiview image generation
supervised generative adversarial network (GAN)
Journal
I
IF:
2.2
Papers:
354
Citations:
3.1K