# Real-Time Analysis of Facial Expressions for Mood Estimation
## 6 Conclusions  
This paper presented a novel approach for the real-time estimation of mood from facial expressions. The main novelty regards the mapping of AUs into the PAD space, obtaining consistent sets of AUs that describe different expressions for each mood. The other contribution is the real-time recognition of mood in spontaneous facial expressions in long video sequences. We also present a mood data set annotated by the users themselves by means of a self-assessment questionnaire. The data set contains video examples of natural reactions to audio-visual stimuli similar to the ones used to conduct research studies with focus groups. The results obtained in the conducted experiments show that Dominance is the \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Mood (SAM)** & **Dimensions** & **Coincidences** & **Sequences** \\ \hline Positive & P+ & 32 & 33 \\ Negative & P\(-\) & 33 & 36 \\ \hline
**Level 1** & & **65** & **69** \\ \hline Exalted & P+, A+ & 14 & 21 \\ Calm & P+, A\(-\) & 11 & 12 \\ Anxious & P\(-\), A+ & 12 & 16 \\ Bored & P\(-\), A\(-\) & 14 & 20 \\ \hline
**Level 2** & & **51** & **69** \\ \hline Elated & P+, A+, D+ & 11 & 15 \\ Dependent & P+, A+, D\(-\) & 3 & 6 \\ Relaxed & P+, A\(-\), D+ & 9 & 9 \\ Docile & P+, A\(-\), D- & 1 & 3 \\ Hostile & P\(-\), A+, D+ & 3 & 5 \\ Anxious & P\(-\), A+, D\(-\) & 8 & 11 \\ Disdainful & P\(-\), A\(-\), D+ & 2 & 7 \\ Bored & P\(-\), A\(-\), D\(-\) & 7 & 13 \\ \hline
**Level 3** & & **44** & **69** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mood estimation evaluation results. dimension least impacted by facial expression, and this dimension could be eliminated from mood categorization. Then, four categories corresponding to the quadrants of the Pleasure-Arousal plane, \"Exalted\", \"Calm\", \"Anxious\" and \"Bored\" were maintained, with two categories of \"Positive\" and \"Negative\" signs for the Pleasure (P) dimension. The 73% of coincidence in the Pleasure-Arousal categorization and the 94% of coincidence in the Pleasure dimension with the questionnaires filled by the participants show that mood can be indeed recognized automatically and perceived subjectively in the particular conditions of research studies. This research represents a step forward in the recognition of affect in facial expressions because it is not limited to the common use of expressions of emotions, but opens a new door for the recognition of different affective traits based on the PAD model. In addition, the presented method, which works in real time and is based on traditional computer vision techniques, could be implemented on less expensive hardware, such as a smartphone, making its implementation more affordable. The source code for the Android OS version is available at the following GitHub repository: [[https://github.com/jsebat/facetomood](https://github.com/jsebat/facetomood)]([https://github.com/jsebat/facetomood](https://github.com/jsebat/facetomood)), accessed on 1 July 2024. In the future, we intend to broaden the set of mapped AUs for a finer detection, as well as the mood estimation in other environments where spontaneous behavior is possible.The ability of recent generative deep learning models to generate synthetic images of faces could address the problem in another way. The possibility of controlling the facial latent space to model facial expressions opens the hypothesis of exploring better representations of facial expressions to facilitate the estimation of affective states since, currently, there is only a certain guarantee of results in the recognition of microexpressions. Conceptualization, J.V. ; methodology, J.S.F., J.V. and C.M.-Y. ; software, J.S.F. ; validation, J.S.F., J.V. and C.M.-Y. ; formal analysis, J.S.F. and J.V. ; investigation, J.S.F., J.V. and C.M.-Y. ; data curation, J.S.F. ; writing--original draft preparation, J.S.F., J.V. and C.M.-Y. ; writing--review and editing, J.S.F., J.V. and C.M.-Y. ; visualization, J.S.F. ; supervision, J.V. and C.M.-Y. ; project administration, J.S.F. and J.V. ; funding acquisition, C.M.-Y.