A deep neural network for hand gesture recognition from RGB image in complex background

Deep learning research has gained significant popularity recently, finding applications in various domains such as image preprocessing, segmentation, object recognition, and semantic analysis. Deep learning has gradually replaced traditional algorithms such as color-based methods, contour-based methods, and motion-based methods. In the context of hand gesture recognition, traditional algorithms heavily rely on depth information for accuracy, but their performance is often subpar. This paper introduces a novel approach using a deep neural network for hand gesture recognition, requiring only a single complementary metal oxide semiconductor (CMOS) camera to operate amidst complex backgrounds. The neural network design incorporates depthwise separable convolutional layers, dividing the model into segmentation and recognition components. As our proposed single-stage model, we avoid the use of the whole model and thus reduce the number of weights and calculations. Additionally, in the training phase, the data augmentation and iterative training strategy further increase recognition accuracy. The results show that the proposed work uses little parameter usage while still having a higher gesture recognition rate than the other works.