Imperial College London
Alternate

Professor Anil Anthony Bharath

Faculty of Engineering, Department of Bioengineering

Academic Director (Singapore)
 
 
 
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Contact

 

+44 (0)20 7594 5463a.bharath Website

 
 
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Location

 

4.12Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Gionfrida:2022:10.3390/electronics11152427,
author = {Gionfrida, L and Rusli, W and Kedgley, A and Bharath, A},
doi = {10.3390/electronics11152427},
journal = {Electronics},
title = {A 3DCNN-LSTM multi-class temporal segmentation for hand gesture recognition},
url = {http://dx.doi.org/10.3390/electronics11152427},
volume = {11},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This paper introduces a multi-class hand gesture recognition model developed to identify a set of hand gesture sequences from two-dimensional RGB video recordings, using both the appearance and spatiotemporal parameters of consecutive frames. The classifier utilizes a convolutional-based network combined with a long-short-term memory unit. To leverage the need for a large-scale dataset, the model deploys training on a public dataset, adopting a technique known as transfer learning to fine-tune the architecture on the hand gestures of relevance. Validation curves performed over a batch size of 64 indicate an accuracy of 93.95% (±0.37) with a mean Jaccard index of 0.812 (±0.105) for 22 participants. The fine-tuned architecture illustrates the possibility of refining a model with a small set of data (113,410 fully labelled image frames) to cover previously unknown hand gestures. The main contribution of this work includes a custom hand gesture recognition network driven by monocular RGB video sequences that outperform previous temporal segmentation models, embracing a small-sized architecture that facilitates wide adoption.
AU  - Gionfrida,L
AU  - Rusli,W
AU  - Kedgley,A
AU  - Bharath,A
DO  - 10.3390/electronics11152427
PY  - 2022///
SN  - 2079-9292
TI  - A 3DCNN-LSTM multi-class temporal segmentation for hand gesture recognition
T2  - Electronics
UR  - http://dx.doi.org/10.3390/electronics11152427
UR  - http://hdl.handle.net/10044/1/98865
VL  - 11
ER  -
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