AlexNet based facial expression classification

Yusufu Tureke; Wenshuo Xu; Jikang Zhao

doi:10.1117/12.2640975

13 October 2022 AlexNet based facial expression classification

Yusufu Tureke, Wenshuo Xu, Jikang Zhao

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 1228724 (2022) https://doi.org/10.1117/12.2640975
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

In this paper, we use the image data in the dataset-FER2013 as a sample. First, we format the CSV file in the dataset. After finding that there is a large difference in the number of different types of pictures, in order to compare and verify the impact of the number difference on the results, we enhanced the data set through the proposed data augmentation method, so that the data set can meet the experimental needs. After that, by understanding the artificial intelligence learning algorithms at home and abroad, we decided to establish a training model using AlexNet network, and divided the pictures to be identified into three groups with different numbers: original data set, down sampling data set and dataset with data augmentation. In the experiment, we achieves 52.3% accuracy after data enhancement, and 66.9% accuracy using the down sampling dataset.

Citation Download Citation

Yusufu Tureke, Wenshuo Xu, and Jikang Zhao "AlexNet based facial expression classification", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 1228724 (13 October 2022); https://doi.org/10.1117/12.2640975

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KEYWORDS

Image enhancement

Data modeling

Artificial intelligence

Computer vision technology

Data conversion

Facial recognition systems

Image processing

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