Facial Expression Classification System Using Stacked CNN

Automatic emotion recognition technology through facial expressions has broad potential, ranging from human- computer interaction to stress detection and blood pressure assessment. Facial expressions exhibit patterns and characteristics that can be identified and analyzed by image processing and machine learning methods. These methods provide a basis for the development of emotion recognition systems. This research develops a facial emotion recognition model using Convolutional Neural Network (CNN) architecture, a popular architecture in image classification, segmentation, and object detection. CNNs offer automatic feature extraction and complex pattern recognition advantages on image data. This research uses three types of datasets, FER2013, CK+, and IMED, to optimize the deep learning approach. The developed model achieved an overall accuracy of 71% on the three datasets combined, with an average precision, recall, and F1-Score of 71%. The results show that CNN architecture performed well in facial emotion classification, supporting potential practical applications in various fields.