# Emotion Identification in Movies through Facial Expression Recognition
## 5 Conclusions  
The work described in this paper had as its main objective the definition of an approach for the automatic computation of video-induced emotions using actors' facial expressions. It discusses the main models and theories for representing emotions, discrete and dimensional, along with their respective advantages and limitations. Then, we proceed with the exploration of a theoretical modeling approach from facial expressions to emotions, and discussed a possible approximation between these two very distinctive theories. The contextualization from human and social sciences allowed to foresee that the lack of unanimity in the classification of emotions would naturally have repercussions both in the databases and in the classification models, one of the major bottlenecks of affective analysis. A systematic validation and benchmark analysis was performed to SoA FER approaches applied to the movie domain. After initial benchmarks, we fine-tuned the chosen model with FER2013, evaluating it with the movie-related dataset, SFEW. During this phase, we noticed several flaws and limitations in these datasets, ranging from class imbalance to even some blank images that do not contain faces. Additionally, we studied, through dimensionality reduction, the hypothesis that clustering observations from the valence-arousal space in other domains are transferable to this approach. The obtained results show that even if there are still many open challenges related, among others, to the lack of data in the film domain and to the subjectiveness of emotions, the proposed methodology is capable of achieving relevant accuracy standards. From the work developed and described in this article, several conclusions can be drawn. Firstly, there is a lack of training data both in terms of quantity and quality: there is no publicly available dataset that is large enough for the current deep learning standards. Additionally, within the available databases, there are several inconsistencies in the annotation (using different models of emotion, or even within the same theory of emotion) and image collection processes (illumination variation, occlusions, head-pose variation) that hinder progress in the FER field. Furthermore, the notion of ground truth applied to this context needs to be taken with a grain of salt, since classifying emotion is intrinsically biased in terms of the degree to which it reflects the perception of the emotional experience that the annotator is experiencing. [PERSON]'s basic emotions model is commonly used in current facial expression classification systems, since it tackles the definition of universal emotions and is widely accepted in the social sciences community. This model was designed by empirical experiences within people from different geographical areas, aiming to understand whether the same facial expressions translate a single emotion, without cultural variations. Hence, [PERSON] designed seven basic emotions used nowadays in the technological fields, to identify emotion through facial expressions. Current solutions are now quite accurate in this task for a variety of applications, with recent commercial uses, namely in the social networks. However, specifically in the cinema field, analyzing emotions from characters with existing frameworks proved to be an unsatisfying approach. On the one hand, actors are entitled to rehearse a facial expression of a character in a certain context. In this field, emotional representation is acted, thus using [PERSON]'s model might not be a valid solution for the analysis of cinematographic content. For example, by applying current FER approaches to a comedy movie, the results could be flawed because acted emotions in this context should not be translated literally to the exact emotion apparent in the facial expression.