Vision Transformer-Enhanced Multi-Descriptor Approach for Robust Age-Invariant Face Recognition

Abstract

This study presents a robust age-invariant face recognition framework, addressing challenges posed by age-related facial variations. Evaluated on the FGNet and Morph II datasets, the system integrates Viola-Jones for face detection, SIFT and LBP for feature extraction, and Vision Transformers (ViTs) for global feature representation. Feature fusion and dimensionality reduction (KPCA, IPCA, UMAP) enhance efficiency while retaining key discriminative information. Using Random Forest, KNN, and XGBoost classifiers, the model achieves 96% accuracy, demonstrating the effectiveness of combining traditional and deep learning techniques in advancing age-invariant face recognition.