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Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory
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DOI：10.3390/bdcc7020102.png)
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Gesture recognition is widely used to express emotions or to communicate with other people or machines. Hand gesture recognition is a problem of great interest to researchers because it is a high-dimensional pattern recognition problem. The high dimensionality of the problem is directly related to the performance of machine learning models. The dimensionality problem can be addressed through feature selection and feature extraction. In this sense, the evaluation of a model with manual feature extraction and automatic feature extraction was proposed. The manual feature extraction was performed using the statistical functions of central tendency, while the automatic extraction was performed by means of a CNN and BiLSTM. These features were also evaluated in classifiers such as Softmax, ANN, and SVM. The best-performing model was the combination of BiLSTM and ANN (BiLSTM-ANN), with an accuracy of 99.9912%.
Keyword:
hand gesture recognition
feature selection
leap motion controller
feature extraction
recurrent neural network
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B
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4.4
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被引数:
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