DNA-BASED FACIAL TRAIT PREDICTION AND 3D FACIAL RECONSTRUCTION USING DEEP LEARNING TECHNIQUES

Abstract

Human facial morphology is a complex phenotypic trait influenced by numerous genetic variations and biological factors. This study presents a DNA-based facial trait prediction and 3D facial reconstruction framework that aims to estimate human facial characteristics directly from genomic information. The proposed system processes raw DNA data obtained from formats such as VCF, FASTA, and genotype files, followed by preprocessing, quality assessment, and reference genome alignment to ensure data reliability. Relevant Single Nucleotide Polymorphisms (SNPs) associated with craniofacial features are identified using Genome-Wide Association Studies (GWAS), LASSO regression, and Random Forest feature selection techniques. These selected SNPs are encoded into numerical allele dosage representations and transformed into compact latent embeddings through deep learning-based feature extraction. A neural network predictor is then employed to learn complex genotype-to-phenotype relationships and estimate facial trait coefficients, including facial landmarks, shape descriptors, and principal component parameters. The predicted traits are further utilized by a 3D Morphable Model (3DMM) and neural rendering techniques to reconstruct anatomically consistent facial structures. The proposed framework is expected to achieve accurate facial trait prediction, improved understanding of genetic influences on craniofacial morphology, realistic 3D facial reconstruction, and enhanced genotype-to-phenotype mapping. Additionally, the system has potential applications in forensic science, genetic research, and biological visualization. Overall, the integration of genomic preprocessing, SNP selection, deep neural networks, and facial reconstruction models provides a scalable, interpretable, and efficient solution for predicting facial structures from DNA while maintaining scientific reliability and ethical responsibility.