TY  - EJOU
AU  - Prabhu, K. 
AU  - SathishKumar, S. 
AU  - Sivachitra, M. 
AU  - Dineshkumar, S. 
AU  - Sathiyabama, P. 

TI  - Facial Expression Recognition Using Enhanced Convolution Neural Network with Attention Mechanism
T2  - Computer Systems Science and Engineering

PY  - 2022
VL  - 41
IS  - 1
SN  - 

AB  - Facial Expression Recognition (FER) has been an interesting area of research in places where there is human-computer interaction. Human psychology, 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 popularity in applications of real-world problems including recognition of human emotions. 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 Attention mechanism) to recognize seven types of human emotions with satisfying results in its experiments. Proposed EECNN achieved 89.8% accuracy in classifying the images.
KW  - Facial expression recognition; linear discriminant analysis; animal migration optimization; regions of interest; enhanced convolution neural network with attention mechanism

DO  - 10.32604/csse.2022.019749