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Facial Expression Recognition Using Enhanced Convolution Neural Network with Attention Mechanism 被引量:2
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作者 K.Prabhu S.SathishKumar +2 位作者 M.Sivachitra S.Dineshkumar P.Sathiyabama 《Computer Systems Science & Engineering》 SCIE EI 2022年第4期415-426,共12页
Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER hav... Facial Expression Recognition(FER)has been an interesting area of research in places where there is human-computer interaction.Human psychol-ogy,emotions and behaviors can be analyzed in FER.Classifiers used in FER have been perfect on normal faces but have been found to be constrained in occluded faces.Recently,Deep Learning Techniques(DLT)have gained popular-ity in applications of real-world problems including recognition of human emo-tions.The human face reflects emotional states and human intentions.An expression is the most natural and powerful way of communicating non-verbally.Systems which form communications between the two are termed Human Machine Interaction(HMI)systems.FER can improve HMI systems as human expressions convey useful information to an observer.This paper proposes a FER scheme called EECNN(Enhanced Convolution Neural Network with Atten-tion mechanism)to recognize seven types of human emotions with satisfying results in its experiments.Proposed EECNN achieved 89.8%accuracy in classi-fying the images. 展开更多
关键词 Facial expression recognition linear discriminant analysis animal migration optimization regions of interest enhanced convolution neural network with attention mechanism
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