Experimental Analysis on Detection of Emotions by Facial Recognition using Different Convolution Layers

This Emotions have been playing a crucial part in the social, physical, and mental life. Hence identifying the emotions by recognizing the facial expression of the user is of utmost importance. This information obtained will help in various recommendation systems like music, products, books etc. In this paper, the emotions are detected by using the facial data and the results are compared between CNN model and DCNN model. The dataset used was FER-2013, which included 28,000 annotated photos in the training set, 3,500 in the training set, and 3,500 in the test set. In FER-2013, each image is assigned to one of seven emotions: happy, sad, angry, afraid, surprise, disgust, and neutral. The parameters chosen are applied to three different models created by altering Convolution Layers, demonstrating that as the number of layers grow, accuracy improves.