We have created a Deep Learning network to identify the emotion of a person. It is based on seven facial
expressions. (angry, disgust, sadness, happy, nature, fear, and surprise). We used extended Cohn–Kanade
(CK+) database basis of (10-fold cross-validation) to identify 6 facial expressions. The Deep Learning
Network scored a recognition average of 88.9%. As you can see in the confusion matrix, the expressions
happy and surprised achieved the best recognition rates 98.92 and 97.23 successively. We also used, in
another experiment, the JAFFE database basis of (LOOCV) and it scored a recognition average of 88%. As
you can see in the confusion matrix that the expressions of fear and surprise achieved the best recognition
rates 93.33 and 93.33, respectively. We compared the performance of the proposed system to similar studies
that followed the same databases with the same sample and the same style. The system we used outscored
other systems in the other studies. We also compared in detail the percentage of identification performance
for each expression in isolation using the extended Cohn–Kanade (CK+) database. We compared our study
to other studies and we found that our system did better.
