Facial Landmark-Based Emotion Recognition via Directed Graph Neural Network
Department of Electronic Engineering, Inha University, Incheon 22212, Korea
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Electronics 2020, 9(5), 764; https://doi.org/10.3390/electronics9050764
Received: 8 April 2020
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Revised: 30 April 2020
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Accepted: 2 May 2020
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Published: 6 May 2020
(This article belongs to the Section Artificial Intelligence)
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.
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MDPI and ACS Style
Ngoc, Q.T.; Lee, S.; Song, B.C. Facial Landmark-Based Emotion Recognition via Directed Graph Neural Network. Electronics 2020, 9, 764. https://doi.org/10.3390/electronics9050764
AMA Style
Ngoc QT, Lee S, Song BC. Facial Landmark-Based Emotion Recognition via Directed Graph Neural Network. Electronics. 2020; 9(5):764. https://doi.org/10.3390/electronics9050764
Chicago/Turabian StyleNgoc, Quang T., Seunghyun Lee, and Byung C. Song. 2020. "Facial Landmark-Based Emotion Recognition via Directed Graph Neural Network" Electronics 9, no. 5: 764. https://doi.org/10.3390/electronics9050764
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