British Sign Language Recognition via Late Fusion of Computer Vision and Leap Motion with Transfer Learning to American Sign Language


In this work, we show that a late fusion approach to multimodality in sign language recognition improves the overall ability of the model in comparison to the singular approaches of image classification (88.14%) and Leap Motion data classification (72.73%). With a large synchronous dataset of 18 BSL gestures collected from multiple subjects, two deep neural networks are benchmarked and compared to derive a best topology for each. The Vision model is implemented by a Convolutional Neural Network and optimised Artificial Neural Network, and the Leap Motion model is implemented by an evolutionary search of Artificial Neural Network topology. Next, the two best networks are fused for synchronised processing, which results in a better overall result (94.44%) as complementary features are learnt in addition to the original task. The hypothesis is further supported by application of the three models to a set of completely unseen data where a multimodality approach achieves the best results relative to the single sensor method. When transfer learning with the weights trained via British Sign Language, all three models outperform standard random weight distribution when classifying American Sign Language (ASL), and the best model overall for ASL classification was the transfer learning multimodality approach, which scored 82.55% accuracy.

Publication DOI:
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Uncontrolled Keywords: Late fusion,Multimodality,Sign language recognition,Analytical Chemistry,Biochemistry,Atomic and Molecular Physics, and Optics,Instrumentation,Electrical and Electronic Engineering
Publication ISSN: 1424-8220
Last Modified: 14 May 2024 07:16
Date Deposited: 14 Sep 2020 08:41
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Related URLs: https://www.mdp ... 8220/20/18/5151 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-09-09
Accepted Date: 2020-09-04
Authors: Bird, Jordan J. (ORCID Profile 0000-0002-9858-1231)
Ekárt, Anikó (ORCID Profile 0000-0001-6967-5397)
Faria, Diego R. (ORCID Profile 0000-0002-2771-1713)



Version: Published Version

License: Creative Commons Attribution

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