A novel heteromorphic ensemble algorithm for hand pose recognition

Liu, S., Yuan, X., Feng, W., Ren, A. , Hu, Z., Ming, Z., Zahid, A., Abbasi, Q. H. and Wang, S. (2023) A novel heteromorphic ensemble algorithm for hand pose recognition. Symmetry, 15(3), 769. (doi: 10.3390/sym15030769)

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%.

• Item Type: Articles
• Additional Information: This work was supported in part by the National Natural Science Foundation of China under grant 62201438.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zahid, Mr Adnan and Ren, Dr Aifeng and Abbasi, Professor Qammer
• Authors: Liu, S., Yuan, X., Feng, W., Ren, A., Hu, Z., Ming, Z., Zahid, A., Abbasi, Q. H., and Wang, S.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Symmetry
• Publisher: MDPI
• ISSN: 2073-8994
• ISSN (Online): 2073-8994
• Published Online: 21 March 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Symmetry 15(3): 769
• Publisher Policy: Reproduced under a Creative Commons License