Facial Expression based Down Syndrome Detection Using Neuro Versatile Resolution System

Abstract

Down syndrome is a genetic disorder characterized by distinctive craniofacial morphology and facial expression variations, which makes facial image analysis very helpful in screening for Down syndrome at an early stage. This research proposes a methodological approach to developing a facial expression-based detection system for Down syndrome identification. The proposed approach includes four sequential modules, Facial Feature Preservation Network (FaFPN) for adaptive denoising and illumination correction, Integrated Attention Multi Feature Engine (IAM-FE) for attention-guided segmentation of eyes, nose, mouth and facial contours, Transfer Driven Attention Extractor (TDA-E) for deep phenotype feature generation and Neuro Versatile Resolution System (NeVRS) for multi-resolution classification. All these components are integrated into the developed framework, implemented using Python, Deep Learning (DL) libraries for image processing, feature extraction and classification. According to the experimental results, the proposed model achieved an accuracy of 96.72 %. This result illustrates the high level of diagnostic discrimination and the stability of the proposed method for facial phenotype recognition. In summary, the proposed framework provides a reliable way to automate the diagnosis of Down syndrome facial phenotypes and can be used for future development of explainable and real-time decision-support systems.