Merrouche, SaidBen Merrouche, Imad el hak2024-10-312024-10-312024MM/845http://10.10.1.6:4000/handle/123456789/5669Recognition of human emotions, particularly through facial expressions, has recently garnered a lot of research attention. Advanced deep learning and machine learning techniques have been employed to analyze the CK+ database in order to better understand and identify emotions. In our experiments, we explored two primary methods for emotion detection. The first method involved machine learning techniques using algorithms such as k-nearest neighbors (K-NN) and support vector machines (SVM). The second method relied on deep learning using convolutional neural networks (CNN) and (DenseNet). This comparison allowed us to evaluate the effectiveness of traditional approaches versus modern techniques in the field of emotion recognition, providing us with deep insights into the relative performance of eachfrbiométrie, réseau de neurones con-volutifs (CNN), reconnaissance des emotions faciales (REF), Machines à vecteurs de support (SVM), K-plus proche voisin (KNN), réseau convolutionnel densément connecté (DenseNetbiometrics ,convolutional neural network (CNN), facial emotion recognition (FER), support vector machine(SVM), k-nearest neighbors(KNN), densely connected convolutional neural networks(DenseNet)Thesis