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Robust Hand Shape Features for Dynamic Hand Gesture Recognition Using Multi-Level Feature LSTM
- Source :
- Applied Sciences, Vol 10, Iss 6293, p 6293 (2020), Applied Sciences, Volume 10, Issue 18
- Publication Year :
- 2020
- Publisher :
- MDPI AG, 2020.
-
Abstract
- This study builds robust hand shape features from the two modalities of depth and skeletal data for the dynamic hand gesture recognition problem. For the hand skeleton shape approach, we use the movement, the rotations of the hand joints with respect to their neighbors, and the skeletal point-cloud to learn the 3D geometric transformation. For the hand depth shape approach, we use the feature representation from the hand component segmentation model. Finally, we propose a multi-level feature LSTM with Conv1D, the Conv2D pyramid, and the LSTM block to deal with the diversity of hand features. Therefore, we propose a novel method by exploiting robust skeletal point-cloud features from skeletal data, as well as depth shape features from the hand component segmentation model in order for the multi-level feature LSTM model to benefit from both. Our proposed method achieves the best result on the Dynamic Hand Gesture Recognition (DHG) dataset with 14 and 28 classes for both depth and skeletal data with accuracies of 96.07% and 94.40%, respectively.
- Subjects :
- Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
lcsh:Technology
lcsh:Chemistry
human-computer interaction
020204 information systems
Component (UML)
0202 electrical engineering, electronic engineering, information engineering
Feature (machine learning)
General Materials Science
Segmentation
Pyramid (image processing)
Representation (mathematics)
lcsh:QH301-705.5
Instrumentation
ComputingMethodologies_COMPUTERGRAPHICS
Block (data storage)
Fluid Flow and Transfer Processes
lcsh:T
business.industry
Dynamic Hand Gesture Recognition
Process Chemistry and Technology
Geometric transformation
General Engineering
Pattern recognition
lcsh:QC1-999
Computer Science Applications
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
Gesture recognition
hand shape features
020201 artificial intelligence & image processing
Artificial intelligence
lcsh:Engineering (General). Civil engineering (General)
business
lcsh:Physics
Subjects
Details
- ISSN :
- 20763417
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- Applied Sciences
- Accession number :
- edsair.doi.dedup.....4c4434c924944a58f7b36da676a028d4
- Full Text :
- https://doi.org/10.3390/app10186293