# Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study
## 4 Results
In **(a)**, the PCA scatter plot shows a blend of data points for “happy” and “neutral” emotional states, indicating a significant overlap and lack of clear separability in the personalized dataset. Conversely, **(b)** demonstrates a clearer distinction between the two emotional states in the generalized dataset. This comparison underlines the reason for the observed lack of performance improvement in emotion classification for this subject when using a personalized approach as opposed to a generalized one. To explore the most salient features contributing to precise emotion classification, we computed the impurity-based importance of each feature of the RF model. This feature ranking approach inherently accounts for the correlations and complex nonlinear relationships between features. We plot the impurity-based importance of each feature in Figure 6 for a demonstrative set of three users whose ML models were all trained to predict happy vs. neutral. We observe that the top-ranked features across subjects vastly differ, further supporting the need for model personalization in affective computing. Figure 6: Impurity-based importance of each facial feature according to the RF model for 3 subjects’ personalized models. The model for all 3 subjects is predicting happy vs. neutral. (**a**) The signifi-cance of features for participant 32 demonstrates that specific facial action units (AU12, AU14, AU06 etc. ), specific pose parameters, eye landmarks, and gaze angles are of utmost importance for distin-guishing between the two emotional states (happy vs. neutral). (**b**) Participant number 39 demon-strates that AU06, certain pose and eye landmarks, among other factors, hold significant importance. (**c**) Similarly, participant number 40 highlights the significance of specific eye and pose landmarks, along with particular action units such as AU06 and AU09, in distinguishing between the target emotions. In a further analysis, our personalized and generic ML models, using three distinct approaches, surpassed previous general-purpose models in emotion recognition. Table 3 offers a detailed comparison, showcasing the superior performance of our methods over past approaches. In our comparative analysis of emotion recognition approaches, we examined a range of studies across different datasets to contextualize our findings. [PERSON]'s study achieved a 61.5% accuracy on the DEAP dataset using EEG and physiological signals (PPS), employing traditional signal processing techniques. [PERSON] and [PERSON]'s work, also on the DEAP dataset, attained an 83.5% accuracy using deep learning models that integrated EEG and PPS data, highlighting the efficacy of multimodal deep learning approaches. [PERSON]'s study achieved a notable 90.85% accuracy on the eNTER-FACE'05 dataset, utilizing a combination of speech data and facial images, thereby demonstrating the potential of integrating auditory and visual cues. [PERSON], using a similar dataset, achieved an accuracy of 85.97% with a focus on advanced facial expression analysis techniques. In contrast, our work with the Emogition dataset, primarily using Pose/Facial Landmarks data, yielded accuracies of 88.50% (KNN), 91.78% (RF), and 80.42% (DNN) for the generic models. Our personalized models further improved these figures to 90.48%, 92.66%, and 86.40%, respectively. These results not only underscore the effectiveness of personalized ML models in emotion recognition but also demonstrate their competitive edge in achieving high accuracy compared to generalized approaches. Our study's reliance on Pose/Facial Landmarks data, distinct from the multimodal data used in the aforementioned studies, suggests a unique pathway in emotion recognition, relying less on physiological signals and more on visual cues.