This thesis explores the field of image/video recognition of continuous emotional states, with the goal of improving our understanding of human emotions and the role of non-verbal cues in their expression. This is a critical area of research that has numerous practical applications such as mental health, human-computer interaction, and marketing. One of the most important viewpoint on emotion recognition is the affective state, which can be described by two primary dimensions: arousal and valence. Arousal refers to the intensity or the energy level of the emotion, while valence refers to its pleasantness or unpleasantness. In further details, this thesis is focused on arousal and valence automatic recognition from video frames containing human subjects' faces, by applying machine learning and deep learning techniques.