基于合成图像和深度度量学习的身份感知面部表情识别方法

Q3 Computer Science
Siyuan Zhang, Shiming Xiao, Peng Zhang, Wei Huang
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引用次数: 0

摘要

面部表情识别是一项具有挑战性的任务,因为外部环境和身份特征会直接影响分类结果。针对上述挑战,本文提出了一种身份感知面部表情识别方法,该方法将图像合成技术与深度度量学习相结合,通过在FER任务中创建相同身份下的表情组,对面部图像特征进行比较和分类。我们的方法有三部分。第一部分是生成式对抗网络,旨在学习表达信息并综合表达组。实验结果证实了该方法在FER任务中的有效性和进步性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identity-Aware Facial Expression Recognition Method Based on Synthesized Images and Deep Metric Learning
: Facial expression recognition (FER) is a challenging task because the external environment and identity characteristics could affect the classification results directly. To settle down the above-mentioned challenges, this paper proposed an identity-aware facial expression recognition method which combined images synthesis techniques and deep metric learning, and made facial images features compared then clas-sified by creating expression groups under the same identity in FER task. There are three parts in our method. The first part is a generative adversarial network, which aims to learn expression information and synthesis the expression groups. the state-of-the-art methods, the experimental results confirmed that the proposed-method was effective and progressive in FER task.
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来源期刊
计算机辅助设计与图形学学报
计算机辅助设计与图形学学报 Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
1.20
自引率
0.00%
发文量
6833
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