Pembelajaran Mendalam Pengklasifikasi Ekspresi Wajah Manusia dengan Model Arsitektur Xception pada Metode Convolutional Neural Network

Abstract

Deep learning is a neural network that creates innovations that give computer-implanted problem-solving expertise. One of the principles of computer vision is a detection system with a vision framework that can identify things encountered in the same manner as a human vision system. Using an artificial intelligence-based Convolutional Neural Network (CNN) model with deep learning techniques, we present a face emotion identification system. The categorization of facial expressions will be utilized as the basis for a face recognition system trained using CNN. The applications are intended to use the OpenCV, Keras, and TensorFlow libraries as the backend. We were discussing the study on the best use of xception architectural models in facial expression recognition systems. Based on the results of these tests, the study obtained an increased accuracy value in training and data testing on an xception architecture model trained for facial expressions using the FER-2013 dataset, resulting in an accuracy value of 66% as well as the value of each average for precision (76%), recall (65%), and F1 score (63%).