基于对抗分解卷积网络的自发微表情种类判别
Discrimination of Spontaneous Micro-expression Types Based on Adversarial Decomposition Convolutional Network
吴俊1
作者信息
- 1. 抚州职业技术学院,江西 抚州 344000
- 折叠
摘要
针对自发微表情成分提取困难与分类识别率低的问题,提出对抗分解卷积网络,通过网络间的相互博弈与合作,实现自发微表情成分的提取与分类.将中性人脸作为判别网络的真实样本,自发微表情作为分解网络的输入样本,根据网络间的对抗得到只含有自发微表情成分的输出图像,通过网络迁移实现对自发微表情成分的迁移学习与分类.自发微表情跨库实验结果表明,分类准确率得到提升,具有克服不同数据库中人种、肤色差异的效果.
Abstract
Aiming at the problems of the difficulty in extracting spontaneous micro-expression components and the low classification recognition accuracy,the adversarial decomposition convolutional network is proposed to realize the extraction and classification of spontaneous micro-expression components through game and cooperation between networks.The images of neutral face are used as the real samples of the discriminant network,and images of spontaneous micro-expression are used as inputting samples of the decomposition network.According to the adversary between networks,output images containing only spontaneous micro-expression components can be obtained.Then,transfer learning and classification of spontaneous micro-expression components are realized through the transfer network.The cross database experimental results of spontaneous micro-expression show that the classification accuracy is improved and it has the effect of overcoming the differences of race and skin color in different databases.
关键词
自发微表情/分类/分解/对抗生成网络Key words
spontaneous micro-expression/classification/decomposition/Generative Adversarial Networks引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据