Shuffle Vision Transformer: Lightweight, Fast and Efficient Recognition of Driver Facial Expression

TL;DR

This paper introduces ShuffViT-DFER, a lightweight dual architecture combining CNN and ViT for real-time driver facial expression recognition, achieving high accuracy and efficiency on benchmark datasets.

Contribution

The paper proposes a novel transfer learning-based dual model architecture that fuses CNN and ViT features for improved real-time driver facial expression recognition.

Findings

Outperforms state-of-the-art methods on KMU-FED and KDEF datasets.

Achieves real-time processing suitable for practical applications.

Demonstrates high accuracy and computational efficiency.

Abstract

Existing methods for driver facial expression recognition (DFER) are often computationally intensive, rendering them unsuitable for real-time applications. In this work, we introduce a novel transfer learning-based dual architecture, named ShuffViT-DFER, which elegantly combines computational efficiency and accuracy. This is achieved by harnessing the strengths of two lightweight and efficient models using convolutional neural network (CNN) and vision transformers (ViT). We efficiently fuse the extracted features to enhance the performance of the model in accurately recognizing the facial expressions of the driver. Our experimental results on two benchmarking and public datasets, KMU-FED and KDEF, highlight the validity of our proposed method for real-time application with superior performance when compared to state-of-the-art methods.