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

Edmond Li Ren Ewe¹, Chin Poo Lee², Kian Ming Lim²

¹Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia.

|April 4, 2024

Summary

This summary is machine-generated.

A new Lightweight Attentive VGG16 with Random Forest (LAVRF) model enhances sign language recognition. This model achieves over 99% accuracy on multiple datasets, improving gesture detail capture.

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Area of Science:

Computer Vision
Machine Learning
Human-Computer Interaction

Background:

Sign language recognition faces challenges due to complex hand gestures and fine-grained details.
Existing methods struggle with intricate gesture nuances and data complexity.

Purpose of the Study:

To introduce a novel Lightweight Attentive VGG16 with Random Forest (LAVRF) model for improved sign language recognition.
To address the limitations of current models in capturing detailed hand gestures and handling complex data.

Main Methods:

A streamlined VGG16 architecture integrated with attention modules for focused image region analysis.
Incorporation of a Random Forest classifier for robust handling of high-dimensional features and overfitting reduction.
Hyperparameter optimization using Optuna and hill climbing for efficient configuration discovery.

Main Results:

The LAVRF model achieved exceptional accuracy: 99.98% on American Sign Language, 99.90% on American Sign Language with Digits, and 100% on NUS Hand Posture.
The attention mechanisms enhanced representation learning by focusing on relevant image areas.
The Random Forest classifier demonstrated resilience against noisy data and reduced variance.

Conclusions:

The LAVRF model offers a highly accurate and efficient solution for sign language recognition.
The combination of attention-guided VGG16 and Random Forest effectively captures complex sign language gestures.
This approach significantly advances the field of sign language recognition and accessibility.