Recognition Performance of Facial Expression for the Face’s Partial Regions
Recognition Performance of Facial Expression for the Face’s Partial Regions
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人脸局部区域表情识别性能
DOI：
10.1109/icmlc56445.2022.9941316
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Hironobu Takano
中科院分区：
文献类型：
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作者：
Tomoaki Hirose;Kazuma Yamaguchi;Hironobu Takano
随着人工智能的快速发展，人脸表情自动识别的研究也越来越深入。然而，由于人脸的部分遮挡，使得它不能保持较高的人脸表情识别准确率，因为大多数人脸表情识别方法都是基于整个人脸都可见的假设。因此，本研究的目的是开发一种即使在人脸的一部分被遮挡的情况下也不会降低面部表情识别精度的方法。在本文中，我们使用CK+数据集研究了仅针对眼睛周围区域的面部表情识别的准确性。实验中采用了3D CNN和2-D CNN作为输入，实验结果表明，使用3D CNN或2-D CNN和2-D CNN进行人脸表情识别的准确率得到了提高。因此，面部表情的时间变化对于仅使用眼睛周围区域的面部表情识别是有效的。
With the rapid development of artificial intelligence, automatic facial expression recognition has been intensively investigated. However, it cannot maintain high accuracy of facial expression recognition due to face’s partial occlusion because most of facial expression recognition methods are designed based on the assumption that the entire face is visible. Therefore, the purpose of this study is to develop a method that does not degrade the accuracy of facial expression recognition even if a part of the face is occluded. In this paper, we investigate the accuracy of the facial expression recognition for only the region around the eyes using the CK+ dataset. The 3-D CNN and 2-D CNN with synthetic or subtracted eye images as the input image were adopted in the experiment The experimental results showed that the accuracy of facial expression recognition using the 3-D CNN or 2-D CNN with subtracted eye images were improved. Therefore, the temporal variations of facial expression are effective for the facial expression recognition using only the region around the eyes.