Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory
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 t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/7/2/102 |
| _version_ | 1797596072467496960 |
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| author | Rubén E. Nogales Marco E. Benalcázar |
| author_facet | Rubén E. Nogales Marco E. Benalcázar |
| author_sort | Rubén E. Nogales |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-11T02:45:24Z |
| format | Article |
| id | doaj.art-0c28ebb30c6444cbb280fda6c4f97324 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-11T02:45:24Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-0c28ebb30c6444cbb280fda6c4f973242023-11-18T09:18:46ZengMDPI AGBig Data and Cognitive Computing2504-22892023-05-017210210.3390/bdcc7020102Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with MemoryRubén E. Nogales0Marco E. Benalcázar1Artificial Intelligence and Computer Vision Research Lab, Escuela Politécnica Nacional, Quito 170517, EcuadorArtificial Intelligence and Computer Vision Research Lab, Escuela Politécnica Nacional, Quito 170517, EcuadorGesture 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%.https://www.mdpi.com/2504-2289/7/2/102hand gesture recognitionfeature selectionleap motion controllerfeature extractionrecurrent neural network |
| spellingShingle | Rubén E. Nogales Marco E. Benalcázar Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory Big Data and Cognitive Computing hand gesture recognition feature selection leap motion controller feature extraction recurrent neural network |
| title | Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory |
| title_full | Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory |
| title_fullStr | Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory |
| title_full_unstemmed | Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory |
| title_short | Hand Gesture Recognition Using Automatic Feature Extraction and Deep Learning Algorithms with Memory |
| title_sort | hand gesture recognition using automatic feature extraction and deep learning algorithms with memory |
| topic | hand gesture recognition feature selection leap motion controller feature extraction recurrent neural network |
| url | https://www.mdpi.com/2504-2289/7/2/102 |
| work_keys_str_mv | AT rubenenogales handgesturerecognitionusingautomaticfeatureextractionanddeeplearningalgorithmswithmemory AT marcoebenalcazar handgesturerecognitionusingautomaticfeatureextractionanddeeplearningalgorithmswithmemory |