Facial expression recognition based on feature fusion

Jian Chen

doi:10.1117/12.2574468

24 June 2020 Facial expression recognition based on feature fusion

Jian Chen

Proceedings Volume 11526, Fifth International Workshop on Pattern Recognition; 115260C (2020) https://doi.org/10.1117/12.2574468
Event: Fifth International Workshop on Pattern Recognition, 2020, Chengdu, China

Abstract

In this article, an expression recognition algorithm based on feature fusion was proposed. First, 40 sets of Gabor filters were selected to perform filtering operations on the expression images to enhance the texture features of the expression images, and subsequently, Local Binary Patterns(LBP) operators were used to perform feature extraction on the filtered images output by each Gabor channel to obtain LBP feature maps. Then these characteristic graphs are taken as the input of the convolutional neural network and the convolutional neural network is trained.Finally, the input of the fully connected layer of the trained convolutional neural network was taken out separately as the features of the expression image, and these features are classified and identified using the extreme learning machine algorithm. The experimental results showed that the method in this paper was better than the method using a single feature and can effectively improve the recognition rate in expression recognition.

Citation Download Citation

Jian Chen "Facial expression recognition based on feature fusion", Proc. SPIE 11526, Fifth International Workshop on Pattern Recognition, 115260C (24 June 2020); https://doi.org/10.1117/12.2574468

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KEYWORDS

Convolutional neural networks

Detection and tracking algorithms

Feature extraction

Image filtering

Evolutionary algorithms

Facial recognition systems

Image fusion

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