## 4 Results
### AI-Based Facial and Head Movement Recognition  
The use of AI techniques to analyze facial features and head movement patterns as potential markers for ASD is examined. Focusing on specific facial characteristics and motion-based behaviors, these approaches demonstrate how deep learning and machine learning models offer reliable, non-invasive indicators of ASD, enabling early detection and enhancing diagnostic methodologies. The study by [PERSON] et al. (2024) [45] applied deep learning for feature extraction from children's facial images, targeting facial expressions and micro-expressions often linked to ASD. The methodology of the study involved using five pre-trained convolutional neural network (CNN) models--Xception, VGG16, VGG19, MobileNet, and EfficientNetB0--as feature extractors from facial images of children. The dataset, sourced from Kaggle, consisted of 2940 images divided into autistic and non-autistic categories. Each model was utilized to extract features, which were then combined to create a comprehensive feature set. Logistic regression was employed as a binary classifier to distinguish between the two groups based on the extracted features. The images were preprocessed through normalization and resizing to ensure compatibility with the models, ultimately achieving an accuracy of 88.33% in classifying the images. By training the CNN on a dataset that included a diverse range of facial expressions, the model achieved high accuracy in classifying ASD-related facial traits. Key performance metrics, including sensitivity and specificity, were used to validate the model, which showed strong reliability in identifying ASD indicators. The study concluded that deep learning-based facial analysis can play a significant role in early ASD detection, especially when deep feature extraction is applied to capture subtle facial cues associated with ASD. Following a similar aim but with a focus on movement, [PERSON] et al. (2024) [43] investigated the use of head movement patterns as diagnostic markers of ASD. The study identifies small head movements (kines) from video data by analyzing head angles (pitch, yaw, roll). Using a multi-scale technique, these movements are categorized into patterns called kinemes, which represent actions like nodding or shaking. Histograms of these patterns are created to summarize behavior over time. Speech detection was used to separate movements during speaking and listening, improving the analysis. These features are then used to classify autism using machine learning models which often differ in children with ASD. The collected head movement data were processed through a machine learning model trained to detect ASD-specific movement characteristics. The study employed cross-validation to assess model accuracy, achieving high sensitivity and specificity in identifying ASD through these motion patterns. The results showed that certain head movement behaviors--such as repetitive tilting or unusual fixations--are significantly correlated with ASD, suggesting that non-invasive, movement-based data can be a valuable supplement to ASD diagnosis. [PERSON] et al. (2024) developed a real-time emotion recognition framework that leverages facial expression analysis to detect six emotions: anger, fear, joy, neutral, sadness, and surprise. The system employs a three-stage process involving face identification, feature extraction using an autoencoder, and classification via a deep convolutional neural network (DCNN). Among the tested pre-trained models, Xception achieved the highest performance with 95.23% accuracy, a sensitivity of 93.2%, and an AUC of 91.34%. Additionally, the framework integrates fog and IoT technologies to ensure low latency and location awareness, enabling real-time detection and fast response. This innovative approach highlights the potential of facial expression analysis and deep learning for emotion recognition, providing families and medical experts with an efficient tool to support children with ASD in their daily interactions and emotional well-being.