期刊
ELECTRONICS
卷 9, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/electronics9050764
关键词
facial emotion recognition; facial landmark; graph neural network
资金
- Industrial Technology Innovation Program - Ministry of Trade, Industry and Energy (MI, Korea) [10073154]
- Korea Evaluation Institute of Industrial Technology (KEIT) [10073154] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Facial emotion recognition (FER) has been an active research topic in the past several years. One of difficulties in FER is the effective capture of geometrical and temporary information from landmarks. In this paper, we propose a graph convolution neural network that utilizes landmark features for FER, which we called a directed graph neural network (DGNN). Nodes in the graph structure were defined by landmarks, and edges in the directed graph were built by the Delaunay method. By using graph neural networks, we could capture emotional information through faces' inherent properties, like geometrical and temporary information. Also, in order to prevent the vanishing gradient problem, we further utilized a stable form of a temporal block in the graph framework. Our experimental results proved the effectiveness of the proposed method for datasets such as CK+ (96.02%), MMI (69.4%), and AFEW (32.64%). Also, a fusion network using image information as well as landmarks, is presented and investigated for the CK+ (98.47% performance) and AFEW (50.65% performance) datasets.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据