# Reducing Videoconferencing Fatigue through Facial Emotion Recognition
## 1 Introduction  
Famous speeches such as [PERSON]'s \"I have a dream\" in 1963 or [PERSON]'s election victory speech in 2008 are known for their rhetorical manifestations, the selection of inspiring words and the use of vivid and metaphorical language. However, another important driver for a great speech or presentation is the method by which presenters express their information by using emotions [1]. Presentations which make use of emotional experiences and trigger an emotional response from the listeners are more likely to gain the attention of the audience than compared to emotionless presentations [2]. For example, consumers primarily use emotions rather than information when evaluating brands [3]. Moreover, politicians often express anger and sadness to express empathy and worriedness about the topic at hand [1]. Furthermore, brain researchers have showed that humans have to be emotional in order to be memorable [4]. Emotions in presentations can be expressed with multiple methods: the choice of vocabulary, e.g., using emotional words such as \"sad\", \"cry\", and \"loss\" when expressing sadness; the method the presenter communicates, e.g., aggressively vs. sensitively; or the gestures and facial expressions the presenter makes with his/her body and face, e.g., looking happy, sad, angry, disgusted, or surprised [5]. All of the above conveys the presenter's feelings. In particular, facial emotion recognition is a broad and well-studiedresearch stream which has recently received a lot of attention due to the rising development of deep learning based methods. Thanks to deep neural networks with convolutional neural networks (CNN), recurrent neural networks (RNN), and long short-term memory (LSTM) models in particular, researchers can extract emotions from facial expressions with a high degree of accuracy. For example, a large number of researchers demonstrated that their deep learning based methods perform very well on various facial emotion recognition data sets (see [6] for an overview). In addition, [PERSON] and [PERSON] [7] showed that their framework--a combination of CNN and LSTM--is not only robust and effective on artificial facial expression data sets (actors playing various emotions), but is also robust and effective on data sets collected in the wild. Leveraging facial emotion recognition, some researchers analyzed the relationship between facial expressions and the learning process, especially in e-learning environments, to monitor and measure student engagement [8], to create personalized educational e-learning platforms [9], or to provide personalized feedback to improve the learning experience [10]. Our study joins this research stream by analyzing the relationship between speaker and listener's facial expressions and the quality of the presentations. Such an evaluation could help researchers and practitioners better understand the inherent relationship between emotions and presentations. Moreover, the insights could be used to create better presentations, which renders the presentation content more memorable and fosters better knowledge transfer. Hence, our research aims to answer the question of which sequence of emotions among the audience, expressed through their facial expressions, lead to good presentations and which do not. In particular, we hypothesize that certain combinations of emotions conveyed by the presenter or shown by the audience are indicators of higher perceived presentation quality. The lack of such an analysis is likely due to the difficulty of analyzing facial expressions not only of the presenter but also of the audience. Furthermore, presentations need to be quantified objectively. The current Coronavirus pandemic presents a unique opportunity for such an analysis, as pupils, students, companies, and other organizations are shifting towards online platforms such as Zoom, Microsoft Teams, and Skype, which allow presentations to be held in front of a camera. This makes it easy to determine the presenter's and listener's emotions by using Deep Learning.