不平衡数据集中的面部情感识别

Artificial intelligence and applications (Commerce, Calif.) Pub Date : 2022-05-28 DOI:10.5121/csit.2022.120920

Sarvenaz Ghafourian, R. Sharifi, A. Baniasadi

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引用次数: 1

摘要

近年来，计算机视觉的广泛应用已成为热门。面部情绪识别是计算机视觉研究的领域之一，它在人际交往中起着至关重要的作用。本文解决了情感识别数据集中人脸图像的类内方差问题。我们在增强数据集上测试了系统，包括CK+， EMOTIC和KDEF数据集样本。在使用SMOTETomek方法修改我们的数据集之后，我们观察到比默认方法有所改进。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Facial Emotion Recognition in Imbalanced Datasets

The wide usage of computer vision has become popular in the recent years. One of the areas of computer vision that has been studied is facial emotion recognition, which plays a crucial role in the interpersonal communication. This paper tackles the problem of intraclass variances in the face images of emotion recognition datasets. We test the system on augmented datasets including CK+, EMOTIC, and KDEF dataset samples. After modifying our dataset, using SMOTETomek approach, we observe improvement over the default method.

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Artificial intelligence and applications (Commerce, Calif.)

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