Citation

Tan, Yong Soon and Lim, Kian Ming and Tee, Connie and Lee, Chin Poo and Low, Cheng Yaw (2021) Convolutional neural network with spatial pyramid pooling for hand gesture recognition. Neural Computing and Applications, 33 (10). pp. 5339-5351. ISSN 0941-0643

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Abstract

Hand gesture provides a means for human to interact through a series of gestures. While hand gesture plays a significant role in human–computer interaction, it also breaks down the communication barrier and simplifies communication process between the general public and the hearing-impaired community. This paper outlines a convolutional neural network (CNN) integrated with spatial pyramid pooling (SPP), dubbed CNN–SPP, for vision-based hand gesture recognition. SPP is discerned mitigating the problem found in conventional pooling by having multi-level pooling stacked together to extend the features being fed into a fully connected layer. Provided with inputs of varying sizes, SPP also yields a fixed-length feature representation. Extensive experiments have been conducted to scrutinize the CNN–SPP performance on two well-known American sign language (ASL) datasets and one NUS hand gesture dataset. Our empirical results disclose that CNN–SPP prevails over other deep learning-driven instances.

Item Type:	Article
Uncontrolled Keywords:	Neural networks (Computer science)
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	30 Jun 2021 08:02
Last Modified:	30 Jun 2021 08:02
URII:	http://shdl.mmu.edu.my/id/eprint/8769

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