Multi-scale feature fusion facial expression recognition based on attention mechanism

Yimo Lou

doi:10.1117/12.2680974

8 June 2023 Multi-scale feature fusion facial expression recognition based on attention mechanism

Yimo Lou

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127073K (2023) https://doi.org/10.1117/12.2680974
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Aiming at the problem that the traditional convolutional neural network is easy to lose a lot of useful information in the expression feature extraction stage, and cannot extract the highly discriminative expression feature, which leads to the low expression recognition rate, a method of facial expression recognition based on multi-scale feature fusion attention mechanism is proposed. First, ResNet50 is used to extract the convolutional feature. In order to avoid the loss of expression feature information, multi-scale feature fusion is performed on the output feature images of different convolution layers in the network, and more abundant expression feature information is extracted while introducing context information; In order to focus on the key expression features, an improved hybrid attention mechanism is introduced into the network to learn the weight information from channel and spatial domain, obtain the attention map, enhance the expression ability of features, and suppress the impact of redundant information; In order to further improve the discriminability of the extracted expression features, pyramid convolution is introduced to improve the residual network structure. The experimental results show that the average accuracy of the model on the RAF-DB dataset is 83.67%.

Citation Download Citation

Yimo Lou "Multi-scale feature fusion facial expression recognition based on attention mechanism", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127073K (8 June 2023); https://doi.org/10.1117/12.2680974

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KEYWORDS

Convolution

Facial recognition systems

Feature extraction

Feature fusion

Neural networks

Visual process modeling

Detection and tracking algorithms

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