Convolutional neural network with spatial pyramid pooling for hand gesture recognition

Yong Soon Tan, Kian Ming Lim*, Connie Tee, Chin Poo Lee, Cheng Yaw Low

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

68 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)5339-5351
Number of pages13
JournalNeural Computing and Applications
Volume33
Issue number10
DOIs
Publication statusPublished - May 2021
Externally publishedYes

Keywords

  • Convolutional neural network (CNN)
  • Hand gesture recognition
  • Sign language recognition
  • Spatial pyramid pooling (SPP)

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