Abstract
Abstract With the continuous development and progress of the times, the ways of human-computer interaction have become more and more diverse. In order to reduce the spread of the new crown virus, gesture recognition has become a hot topic in the field of human-computer interaction in recent years. Traditional gesture recognition is affected by the environment and database, etc., with poor robustness and low accuracy. In order to improve the recognition rate of static gestures, this paper proposes to establish a deep learning model using CNN convolutional neural network, and a static gesture recognition method based on template matching. By establishing a palm template diagram, the gesture image to be recognized is matched with the template diagram based on the feature point, and the image is rotated after matching, and the template based on the grayscale value is matched again, so as to extract the gesture part. Through experimental proof, the algorithm can effectively improve the gesture recognition rate, the recognition accuracy rate reached 93.17%, and the recognition speed is faster.
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