An Intelligent Dipsomania Face Detection Model using Spatial and Spectral Graph Neural Network

Dipsomania, alcohol addiction that is out of control poses serious problems for public health. Conventional techniques for identifying drunk people depend on behavioral indicators, which are easily manipulated. This article suggests a novel method by employing spectral and spatial graph neural networks (SS-GNN) to create an intelligent face identification model for dipsomania. Facial traits are stored as nodes and edges in a graph structure, and the model uses the special powers of graph neural networks to process and analyses these features. The SS-GNN model that has been suggested is trained on a wide range of facial image datasets that include both drunken and sober states. Advanced methods of data augmentation and domain adaptation are utilized to enhance the model’s resilience and applicability to various demographics and environmental circumstances. By combining spectral and spatial information into a graph-based framework, dipsomania-related facial traits can be better understood. This innovative method provides a noninvasive, dependable means of early diagnosis and intervention in cases of alcohol addiction and shows potential for real-time applications in public safety, healthcare, and workplace monitoring.