Citation

Ewe, Edmond Li Ren and Lee, Chin Poo and Lim, Kian Ming and Kwek, Lee Chung and Alqahtani, Ali (2024) LAVRF: Sign language recognition via Lightweight Attentive VGG16 with Random Forest. PLOS ONE, 19 (4). e0298699. ISSN 1932-6203

Text journal.pone.0298699.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

Sign language recognition presents significant challenges due to the intricate nature of hand gestures and the necessity to capture fine-grained details. In response to these challenges, a novel approach is proposed—Lightweight Attentive VGG16 with Random Forest (LAVRF) model. LAVRF introduces a refined adaptation of the VGG16 model integrated with attention modules, complemented by a Random Forest classifier. By streamlining the VGG16 architecture, the Lightweight Attentive VGG16 effectively manages complexity while incorporating attention mechanisms that dynamically concentrate on pertinent regions within input images, resulting in enhanced representation learning. Leveraging the Random Forest classifier provides notable benefits, including proficient handling of high-dimensional feature representations, reduction of variance and overfitting concerns, and resilience against noisy and incomplete data. Additionally, the model performance is further optimized through hyperparameter optimization, utilizing the Optuna in conjunction with hill climbing, which efficiently explores the hyperparameter space to discover optimal configurations. The proposed LAVRF model demonstrates outstanding accuracy on three datasets, achieving remarkable results of 99.98%, 99.90%, and 100% on the American Sign Language, American Sign Language with Digits, and NUS Hand Posture datasets, respectively.

Item Type:	Article
Subjects:	Q Science > QC Physics > QC474-496.9 Radiation physics (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 May 2024 04:00
Last Modified:	02 May 2024 04:01
URII:	http://shdl.mmu.edu.my/id/eprint/12396

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