# Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study
## 2 Prior Work and Background  
In the expansive realm of digital health, facial emotion recognition has emerged as a focal point of research, presenting an array of applications across various health domains. This dynamic field faces significant challenges rooted in the inherent subjectivity of observer perception, influenced by factors such as gender, age, and ethnicity. The intricate task of accurately interpreting the diverse spectrum of human facial expressions adds complexity to this endeavor [19; 20; 21]. Within this multifaceted landscape, the application of facial emotion recognition holds particular relevance in understanding and addressing health challenges faced by diverse populations. One noteworthy area is autism spectrum disorder (ASD), where individuals often encounter difficulties in recognizing and interpreting facial expressions. This unique challenge intersects with the broader complexities in facial emotion recognition, underscoring the need for nuanced approaches to cater to the diverse needs of individuals on the autism spectrum. One family of approaches to building affective computing models involves the extraction of domain-specific features which are fed as input into classical ML models. For example, the local binary pattern (LBP) method transforms an image into a configuration of micro-patterns [22; 23]. In 2009, [PERSON] et al. [24] evaluated the effectiveness of LBP features in recognizing expressions. The results of the experiment confirmed that LBP features possess a certain level of efficiency. However, despite their usefulness, facial expression recognition methods based on LBP also suffer from challenges including low accuracy in recognition and vulnerability to interference [25]. Another feature extraction approach involves the use of Histogram of Oriented Gradients (HoG) features to recognize facial expressions. In [PERSON] et al. 's study, a combination of feature extractors, such as LBP, PCA, and HoG, was used together with a SVM classifier to categorize static face images into six distinct emotions [26]. In a study conducted in 2012, [PERSON] et al. [27] used Haar wavelet features along with multiscale analysis and statistical analysis to recognize facial expressions. However, the use of Haar-based facial expression recognition presents challenges, including a high rate of false recognition and the incomplete extraction of facial expression information. Another method called scale-invariant feature transform (SIFT) was used by [PERSON] et al. [28] in 2011 to describe face pose and achieve expression recognition through the extraction of principal component information using singular value decomposition (SVD). Nevertheless, using the SIFT-based method for expression recognition faces obstacles such as limited computational efficiency and vulnerability to dimensionality problems. An alternative strategy for recognizing facial expressions involves deep learning. In 2006, [PERSON] introduced the layer-by-layer training approach to tackle the complex task of training neural networks with multiple layers [29]. As a result, robust open-source learning frameworks such as Torch, Caffe, Deep Learn Toolbox, and Cxnet were created, supported by substantial contributions from researchers and institutions. Deep learning has the capability to approximate high-dimensional data spaces, making it well suited for learning intricate functions and extracting high-dimensional feature representations from images. While initially used for object image classification, deep learning has gradually found applications in face recognition as well [15; 30; 31]. In 2016, [PERSON] and colleagues presented a new method for recognizing facial expressions that are invariant to attitude. Their approach involved combining deep learning techniques, a principal component analysis network, and CNN. Through extensive experiments on two publicly available databases, they demonstrated substantial enhancements in their method compared to traditional techniques used for expression recognition [32].