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LAVRF: Sign language recognition via Lightweight Attentive VGG16 with Random Forest

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  • Edmond Li Ren Ewe
  • Chin Poo Lee
  • Kian Ming Lim
  • Lee Chung Kwek
  • Ali Alqahtani

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.

Suggested Citation

  • Edmond Li Ren Ewe & Chin Poo Lee & Kian Ming Lim & Lee Chung Kwek & Ali Alqahtani, 2024. "LAVRF: Sign language recognition via Lightweight Attentive VGG16 with Random Forest," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-22, April.
  • Handle: RePEc:plo:pone00:0298699
    DOI: 10.1371/journal.pone.0298699
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    References listed on IDEAS

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    1. Ravinder Ahuja & Daksh Jain & Deepanshu Sachdeva & Archit Garg & Chirag Rajput, 2019. "Convolutional Neural Network Based American Sign Language Static Hand Gesture Recognition," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 10(3), pages 60-73, July.
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