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Fingerspelling Recognition by 12-Layer CNN with Stochastic Pooling

journal contribution
posted on 06.05.2022, 10:44 by YD Zhang, X Jiang, SH Wang

Fingerspelling is a method of spelling words via hand movements. This study aims to propose a novel fingerspelling recognition system. We use 1320 fingerspelling images in our dataset. Our method is based on the convolutional neural network (CNN) model. We propose a 12-layer CNN as the backbone. Particularly, stochastic pooling (SP) is used to help solve the problems caused by max pooling or average pooling. In addition, an improved 20-way data augmentation method is proposed to circumvent overfitting. Our method is dubbed CNNSP. The results show that our CNNSP method achieved a micro-averaged F1 (MAF) score of 90.04 ± 0.82%. In contrast, the MAFs of l2-pooling, average pooling, and max pooling are 86.21 ± 1.12%, 87.54 ± 1.39%, and 89.07 ± 0.78%, respectively. Our CNNSP attains better results than eight state-of-the-art fingerspelling recognition methods. Besides, the SP is better than l2-pooling, average pooling, and max pooling.

Funding

Hope Foundation for Cancer Research, UK (RM60G0680)

Royal Society International Exchanges Cost Share Award, UK (RP202G0230)

Medical Research Council Confidence in Concept Award, UK (MC_PC_17171)

Global Challenges Research Fund (GCRF), UK (P202PF11)

Sino-UK Industrial Fund, UK (RP202G0289)

British Heart Foundation Accelerator Award, UK (AA/18/3/34220)

History

Citation

Mobile Netw Appl (2022). https://doi.org/10.1007/s11036-021-01900-8

Author affiliation

School of Computing and Mathematical Sciences, University of Leicester

Version

AM (Accepted Manuscript)

Published in

Mobile Networks and Applications

Publisher

Springer Science and Business Media LLC

issn

1383-469X

eissn

1572-8153

Acceptance date

15/11/2021

Copyright date

2022

Available date

21/02/2023

Language

en