# Predicting Dog Emotions Based on Posture Analysis Using DeepLabCut
## 5 Discussion  
Multiple models were developed to determine a dog's emotional state based on input images with an accuracy between 62.5% and 67.5%. Relying on the scientific findings of [PERSON] et al. [3], this degree of emotion recognition capabilities in dogs exceeds the human one. Image-based emotion recognition promises wide everyday application for automatic emotion analysis of animals. In contrast to sensor-based emotion detection [8; 9], the method explored in this work is more cost-efficient and more practicable since no sensor needs to be attached to the animal.  
The neural network seems to be better suited for practical applications since it can handle missing landmark points and therefore provides results even for low-quality input images. The Decision Tree can be used not only for prediction, but also for a better understanding of how dogs express emotions.  
Nevertheless, several aspects could be improved to increase emotion recognition accuracy further. Since the dataset contains only 100 images per class, an extension could contribute to the better generalizability of the model. Additionally, the chosen stimuli-based labeling approach of training data described in Chapter 2.1 could be questioned and possibly replaced by expert labeling.  
Additional emotion classes could be introduced to provide more precise information about the dog's mood. In particular, the emotion \"happiness\" shows signs of high ambiguity for which a split into several categories might be suitable.  
This work focuses mainly on posture and less on facial expression. Therefore, more pose metrics focusing on the facial landmark points could be added.  
When comparing the developed emotion predictor to sensor-based canine emotion detection [8; 9], it becomes apparent that photography induced pose analysis currently cannot compete with the more accurate information that can be received from sensor data. The sound produced by the animal might also be of interest. For example, refs. [10; 30] have already successfully conducted studies that analyzed dog banks in terms of their emotional content. A video recording could capture both the image and the sound information and would allow us to feed more data into the emotion recognition. Nonetheless, the exclusively image-based emotion recognition is still highly relevant since its method of operation promises a wider everyday application for automatic emotion analysis of animals. Additionally, the photographic method can be assumed to be more cost efficient and less disturbing to the animal than the sensor-based analysis.  
Conceptualization, K.F. and P.A.G.; methodology, K.F.; software, K.F.; validation, K.F. and T.S.; data curation, K.F.; writing--original draft preparation, K.F. and T.S.; writing--review and editing, T.S. and P.A.G.; supervision, P.A.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & \
\begin{tabular}{c} **Explanatory** \\ **Abilities** \\ \end{tabular} &
\begin{tabular}{c} **Ability to Deal with** \\ **Missing Data** \\ \end{tabular} \\ \hline Neural Net & 67.5\% & - & ✓ \\ Decision Tree & 62.5\% & ✓ & - \\ Logistic Regression & 62.5\% & - & - \\ Support Vector Machine & 67.5\% & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Overview of model performance.