期刊论文

Convolutional Neural Network Fusing Ranking and Regression for Expression Intensity Estimation

Chen, J.

中文

计算机辅助设计与图形学学报

1003-9775

2020

32

8

1228-1235

10.3724/SP.J.1089.2020.17753

2018YFB1004504:国家重点研发计划 61977027:国家自然科学基金 CCNU18KFY02:中央高校基本科研业务费专项 CCNU19Z02002:中央高校基本科研业务费专项

本校为第一且通讯机构

表情强度估计是面部表情分析的重要组成部分,是实现人机自然情感交互的关键技术.表情强度估计面临的主要挑战在于缺乏大量的有标签数据,难以通过有监督的方法来估计表情强度.尽管基于排序的方法能够解决这一问题,但是排序方法只能估计表情的相对强度,无法估计表情的绝对强度.为了解决上述问题,提出了一种融合排序与回归的卷积神经网络用于表情强度估计.其中,排序卷积神经网络采用孪生网络结构,用于学习序列中任意两帧图像的相对强弱关系;孪生网络的每一个子网采用回归卷积神经网络,用于学习有强度标签的样本,从而估计表情的绝对强度.为了验证方法的有效性,在公共数据集PAIN和CK+上进行了实验.实...

Facial expression intensity estimation is an important part of facial expression analysis. It is also the key technology to realize nature human-machine emotional interaction. The main problem faced by the expression intensity estimation is the lack of abundant labeled data, which makes it difficult to estimate facial expression intensity by supervised methods. Although some ranking based methods can address this problem, these methods only estimate the relative intensity instead of an absolute intensity. To solve the above problems, a convolut...