A Novel Heteromorphic Ensemble Algorithm for Hand Pose Recognition

Liu, Shiruo; Yuan, Xiaoguang; Feng, Wei; Ren, Aifeng; Hu, Zhenyong; Ming, Zuheng; Zahid, Adnan; Abbasi, Qammer H.; Wang, Shuo

doi:10.3390/sym15030769

Open AccessArticle

A Novel Heteromorphic Ensemble Algorithm for Hand Pose Recognition

by

Shiruo Liu

¹,

Xiaoguang Yuan

^1,*

,

Wei Feng

¹,

Aifeng Ren

¹

,

Zhenyong Hu

¹,

Zuheng Ming

²,

Adnan Zahid

³,

Qammer H. Abbasi

³

and

Shuo Wang

¹

School of Electronic Engineering, Xidian University, Xi’an 710071, China

²

Laboraory of Information Processing and Transmission, L2TI, Institut Galilée, University Paris XIII, 93079 Villetaneuse, France

³

School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK

^*

Author to whom correspondence should be addressed.

Symmetry 2023, 15(3), 769; https://doi.org/10.3390/sym15030769

Submission received: 16 February 2023 / Revised: 12 March 2023 / Accepted: 15 March 2023 / Published: 21 March 2023

(This article belongs to the Section A: Computer Science)

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Abstract

Imagining recognition of behaviors from video sequences for a machine is full of challenges but meaningful. This work aims to predict students’ behavior in an experimental class, which relies on the symmetry idea from reality to annotated reality centered on the feature space. A heteromorphic ensemble algorithm is proposed to make the obtained features more aggregated and reduce the computational burden. Namely, the deep learning models are improved to obtain feature vectors representing gestures from video frames and the classification algorithm is optimized for behavior recognition. So, the symmetric idea is realized by decomposing the task into three schemas including hand detection and cropping, hand joints feature extraction, and gesture classification. Firstly, a new detector method named YOLOv4-specific tiny detection (STD) is proposed by reconstituting the YOLOv4-tiny model, which could produce two outputs with some attention mechanism leveraging context information. Secondly, the efficient pyramid squeeze attention (EPSA) net is integrated into EvoNorm-S0 and the spatial pyramid pool (SPP) layer to obtain the hand joint position information. Lastly, the D–S theory is used to fuse two classifiers, support vector machine (SVM) and random forest (RF), to produce a mixed classifier named S–R. Eventually, the synergetic effects of our algorithm are shown by experiments on self-created datasets with a high average recognition accuracy of 89.6%.

Keywords: behavior feature extraction; deep learning; hand pose recognition; multi-classification

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MDPI and ACS Style

Liu, S.; Yuan, X.; Feng, W.; Ren, A.; Hu, Z.; Ming, Z.; Zahid, A.; Abbasi, Q.H.; Wang, S. A Novel Heteromorphic Ensemble Algorithm for Hand Pose Recognition. Symmetry 2023, 15, 769. https://doi.org/10.3390/sym15030769

AMA Style

Liu S, Yuan X, Feng W, Ren A, Hu Z, Ming Z, Zahid A, Abbasi QH, Wang S. A Novel Heteromorphic Ensemble Algorithm for Hand Pose Recognition. Symmetry. 2023; 15(3):769. https://doi.org/10.3390/sym15030769

Chicago/Turabian Style

Liu, Shiruo, Xiaoguang Yuan, Wei Feng, Aifeng Ren, Zhenyong Hu, Zuheng Ming, Adnan Zahid, Qammer H. Abbasi, and Shuo Wang. 2023. "A Novel Heteromorphic Ensemble Algorithm for Hand Pose Recognition" Symmetry 15, no. 3: 769. https://doi.org/10.3390/sym15030769

APA Style

Liu, S., Yuan, X., Feng, W., Ren, A., Hu, Z., Ming, Z., Zahid, A., Abbasi, Q. H., & Wang, S. (2023). A Novel Heteromorphic Ensemble Algorithm for Hand Pose Recognition. Symmetry, 15(3), 769. https://doi.org/10.3390/sym15030769

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