## 4 Related Work in the ML and DL Domain
### Facial Image-Based Diagnosis of ASD  
The diagnosis of ASD has also been explored by examining facial characteristics [74]. Many facial distances are altered in ASD individuals, according to research: the distances between the glabella and nasion and the inner canthi, as well as the distances between nasion and landmarks on the nose and philtrum, are reduced, while the distances between the mouth and the nasal region, as well as the distances between facial landmarks and the eyes and the opposing side that contains the mouth, are increased. Boys with ASD usually exhibit significant facial asymmetry, particularly around the supra- and periorbital areas [75; 76]. Additionally, studies reveal that compared to children without ASD, who more often display expressions indicating engagement and interest, kids who suffer from high-functioning autism (HFA) produce neutral emotions with greater frequency and show less richness and flexibility in their facial expressions. Regarding real-time mental and emotional state assessments and ASD diagnosis, facial recognition technology is beneficial. This technique extracts characteristics that differentiate between regular and aberrant expressions by analyzing human traits and is applied to identify behavioral patterns from big datasets. The study of facial expressions in individuals with ASD has recently received more attention. Facial expressions have also been widely studied in individuals with neurological illnesses such as Alzheimer's disease, neurodegenerative disorders, and frontotemporal dementia. Various techniques teach models to detect characteristics like lips, eyes, and eyebrows to help children with ASD comprehend and use facial emotions. Combined with iconized images, these models help in differential diagnosis [74; 77]. The following is a list of the most relevant works related to AI techniques and the use of facial image data to detect ASD. In 2016, [PERSON] et al. [78] identified children with ASD relying on ML techniques to analyze data on eye movements using a face recognition test. They used a dataset including three cohorts: 29 Chinese children between four and eleven years old diagnosed with ASD, 29 Chinese children matched by age who were typically developed (TD), and 29 additional TD children matched by IQ. Its main objective was to assess the sensitivity, specificity, and accuracy of ASD categorization using patterns from face scans. Later, in 2021, [PERSON] and [PERSON] [79] used the VGG16 DL model with transfer learning based on facial images for identifying ASD in children. This study employed two datasets: \begin{table}
\begin{tabular}{c l l l l l} \hline
**Citation** & **Dataset** & **Technique** & **Reported Metrics** & **Limitations** \\ \hline \multirow{8}{*}{[71]} & ASD datasets for & \multirow{8}{*}{DT, kNN, LR, NB, RF,} & Accuracy: & 100.00\% & \multirow{8}{*}{Class imbalance, no sampling techniques, and lack of feature extraction.} \\  & children, adult, & & & & \\  & teenagers, and & & & & \\  & adolescent, & SVM & & & \\  & Questionnaire based & & & & \\  & datasets: Public & & & & \\ \hline \multirow{4}{*}{[72]} & Self-collected ASD & \multirow{4}{*}{LSTM} & Accuracy: & 97.00\% & \multirow{4}{*}{Unspecified dataset size, missing feature interpretation, and real time detection.} \\  & dataset based on & & & & \\  & sentiments: Private & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{[73]} & ASD Children and & \multirow{4}{*}{NB, kNN, LR, SVM,} & Accuracy: & 94.28\% & \multirow{4}{*}{Small size of data, lack of augmentation, and diversity constraints.} \\  & Adult dataset, & & & & \\  & questionnaire-based & & & & \\  & instances: Public & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._the Kaggle Autism Facial Dataset, which included 2936 photos from diverse racial origins, and the East Asia ASD Children Facial Image Dataset, which included 1122 photographs of East Asian children. The two datasets are split equally between kids with ASD and TD.