Vehicle interior emotion recognition based on attention mechanism and improved ResNet

Yuntao Yue; Lü Zhang; Junhu Lu

doi:10.1117/12.3011563

8 November 2023 Vehicle interior emotion recognition based on attention mechanism and improved ResNet

Yuntao Yue, Lü Zhang, Junhu Lu

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129230M (2023) https://doi.org/10.1117/12.3011563
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

A vehicle interior emotion image classification model is proposed to enhance facial expression recognition accuracy in vehicle interiors. The model is based on attention mechanism and improved ResNet. It incorporates transfer learning with a pretrained ResNet50 model as the base architecture. A Coordinate Attention (CA) mechanism module is inserted after the max pooling layer to combine feature maps and focus on channel attention and spatial relationships. CA attention modules are added after each layer to enhance feature map expressiveness. To address class imbalance in the dataset, data augmentation and weighted loss functions are used for model optimization. Experimental results show improved recognition performance on the KMU-FED dataset with a simple and implementable structure.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuntao Yue, Lü Zhang, and Junhu Lu "Vehicle interior emotion recognition based on attention mechanism and improved ResNet", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129230M (8 November 2023); https://doi.org/10.1117/12.3011563

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KEYWORDS

Education and training

Facial recognition systems

Emotion

Matrices

Convolution

Feature fusion

Image fusion

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