Facial Landmark-Based Emotion Recognition via Directed Graph Neural Network

Quang Tran Ngoc · Seunghyun Lee · Byung Cheol Song

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Emotion and Mood Recognition Face recognition and analysis Face and Expression Recognition

Abstract

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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References

1952Stochastic Estimation of the Maximum of a Regression Function 2016Going deeper in facial expression recognition using deep neural networks 2015Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition 2018Spatial–Temporal Recurrent Neural Network for Emotion Recognition 2018Emotion recognition using deep learning approach from audio–visual emotional big data 2018Facial Expression Recognition with Inconsistently Annotated Datasets 2015Deeply Learning Deformable Facial Action Parts Model for Dynamic Expression Analysis 2017Identity-Aware Convolutional Neural Network for Facial Expression Recognition

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2022Development of Real-Time Landmark-Based Emotion Recognition CNN for Masked Faces 2023Role of Zoning in Facial Expression Using Deep Learning

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