# Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study
## 2 Prior Work and Background
In 2017, [PERSON] [33] introduced in their study an algorithm for extracting facial expressions using deep learning. They conducted an analysis of the existing approaches in this field and compared different methods. The findings revealed that deep learning techniques excel at extracting hierarchical features and leveraging them for image classification based on expressions. Consequently, these methods significantly improve the recognition accuracy when compared to conventional approaches [34; 35; 36]. Several research studies rely on facial images as a primary focus. For instance, [PERSON] et al. [37] used transfer learning for emotion recognition with a MobileNet model. The experimental outcomes demonstrated an accuracy of 89% and an F1 score of 87%. Similarly in 2022, [PERSON] et al. [38] used three pre-trained models, including MobileNet, Xception, and Inception V3, to detect ASD based on facial features. The accuracies were 95%, 94%, and 89% for MobileNet, Xception, and Inception, respectively. Another study by [PERSON] et al. in 2021 [39] enhanced MobileNet V1 by adding layers to improve performance, achieving a classification accuracy of 90.67%. Various studies have also analyzed the facial images of autistic children for diverse purposes. For example, in 2021, [PERSON] et al. [40] developed a deep learning (DL) model to recognize attention from facial analysis, achieving an 88.89% accuracy and 53.1% in terms of the ACC and AUC, respectively. [PERSON] et al. [41] conducted a study on automated emotion classification for children using a gamified approach with the GuessWhat smartphone game. The resulting extensive pediatric emotion-centric database facilitated the training of a CNN classifier, achieving a balanced accuracy of 66.9% and an F1 score of 67.4% on the entire dataset (CAFE). Notably, in a subset with at least 60% human agreement, the classifier achieved a 79.1% balanced accuracy and a 78.0% F1 score, showcasing significant improvements over previous classifiers. [PERSON] et al. [42] conducted a study evaluating the suitability of off-the-shelf emotion classifiers from Microsoft, Amazon, Google, and Sighthound for pediatric populations, specifically children with parent-reported ASD. Using the Guess What? mobile game, 21 children with ASD engaged in social interactions, producing 2602 emotive frames for evaluation. The study revealed that while these classifiers performed well for happy emotions, their accuracy was notably poor for other emotions, indicating a need for improved training data before integrating them into AI-enabled social therapy for ASD treatment. In the context of children with ASD, several other studies use DL and CNN for diagnosis based on facial analysis. In 2020, [PERSON] et al. [43] introduced a DL model to classify children as normal or potentially autistic, achieving an accuracy of 94.6% using the pre-trained MobileNet model. In 2021, [PERSON] et al. [44] compared the accuracy of responses to six emotions (neutral, sad, disgust, anger, fear, and surprise) in normal and autistic children under non-timed and timed conditions. The results indicated that children with autism are less accurate in identifying surprise and anger compared to their normal counterparts. For more comprehensive insights into emotion recognition among individuals with ASD, more expansive review papers, such as that by [PERSON] et al. [45], provide detailed information. Researchers have advocated diverse architectural modifications to CNNs, such as by incorporating the integration of attention mechanisms [46]. The visual attention mechanism enables models to concentrate on specific image regions, enhancing the overall performance. Taking inspiration from the triumph of transformer networks in natural language processing (NLP), vision transformers have been introduced. Prominent among these large transformer-based models are ViT [47], Swin [48], MobileViT [49], BiT [50], and ConvNeXt [51]. Vision transformers use attention mechanisms to encompass global context and extract intricate features from image patches.