Analysis of the automatic gesture recognition process using artificial intelligence technologies

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Abstract

The rapid and dynamic development of artificial intelligence (AI) technologies has significantly advanced the human-machine gesture interface, providing a substantial impetus for solving the problem of gesture recognition. Real-time hand gesture recognition systems enhance the speed and accuracy of task execution. This article analyzes existing types of artificial neural network methods for gesture recognition, specifically Feedforward Neural Networks (FFNN), Kohonen Neural Networks (KNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). It elucidates their operational processes and characterizes the stages of machine learning involved. A comparative analysis of the advantages and disadvantages of neural network-based gesture recognition systems is presented. It is noted that constructing a hybrid method for real-time hand gesture recognition is more expedient, as hybrid approaches ensure high recognition speed and accuracy.

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About the authors

K. S. Gurbanova

Institute of Information Technology of the Ministry of Science and Education of the Azerbaijan Republic

Author for correspondence.
Email: kemalewamil@gmail.com
ORCID iD: 0000-0002-9234-0066

Chief Specialist, Training-Innovation Centre

Azerbaijan, Baku

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Supplementary files

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2. Fig. 1. Overlapping of hands (a); determination of the dominant hand (b)

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3. Fig. 2. Information flow in an artificial neural network

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4. Fig. 3. Neuron cell (a); diagram of the biological neuron and ANN operation (b)

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5. Fig. 4. Architectures of unidirectional (a) and bidirectional (b) FFNN neural networks

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6. Fig. 5. Overall architecture of KNN

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7. Fig. 6. Conversion of the original image into binary code

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8. Fig. 7. Architecture of Convolutional Neural Networks

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9. Fig. 8. Node points of the palm and finger bones

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10. Fig. 9. Architecture diagram of a Recurrent Neural Network

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