Chinese gesture language recognition method based on convolutional neural network

Abstract

The invention relates to a Chinese gesture language recognition method based on a convolutional neural network. The method comprises the following steps of collecting various gesture pictures of Chinese gesture language, and obtaining multiple gesture samples through gesture segmentation and preprocessing; dividing an obtained gesture sample dataset into a training set, a verifying set and a testing set according to the proportion of 5:1:1; building seven layers of convolutional neural network CNN models, wherein the convolutional neural network CNN models include three convolutional layers, two pooling layers and one fully-connecting layer; using the gesture features of the CNN model training set to set the image quantity of batchsize after batching every time, and selecting the featuresafter each convulsion through maximal pooling; after the last convolution of layers, classifying feature vectors through a Softmax function, classifying the result, comparing labels, and updating theweight of the model; comparing the accuracy of the verification set after each iteration, and comparing the result with the last verification result; if the accuracy is lowered, continuing the iteration, and if the accuracy is not lowered, stopping the iteration.

Description

technical field [0001] The invention belongs to the field of gesture recognition. Background technique [0002] At present, gesture recognition methods are mainly divided into two categories. One is the method based on wearable devices. Although the real-time performance is better, wearable devices are expensive and inconvenient to use; the other is based on vision systems. This kind of method collects image information through sensors, and then performs image processing and recognition process, which brings good human-computer interaction to users. How to improve the recognition accuracy and reduce the recognition time has always been the difficulty of this kind of method. [0003] In recent years, with the significant improvement of computer graphics processing capabilities, deep learning has made major breakthroughs in speech recognition, image classification and other fields. Deep learning uses a multi-layer nonlinear deep neural network to classify and process input im...

AI Technical Summary

Problems solved by technology

[0002] At present, gesture recognition methods are mainly divided into two categories. One is the method based on wearable devices. Although the real-time performance is better, wearable devices are expensive and inconvenient to use; the other is based on vision systems. This kind of method collects image information through sensors, and then performs image processing and recognition process, which brings good human-computer interaction to users. How to improve the recognition accuracy and reduce the recognition time is the difficulty of this kind of method all the time.

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