Feature-Driven Fusion of Thermal and Visible Images for Face Analysis

This study presents a feature-driven fusion method for combining thermal and visible images in facial analysis, leveraging morphological and gradient operations to enhance image quality and information content. By integrating diverse features, including structural components and edge details, the proposed fusion technique offers a comprehensive representation of input images, surpassing traditional methods like Discrete Wavelet Transform (DWT) based image fusion. Performance evaluation using metrics such as Peak Signal-to-Noise Ratio (PSNR) and Spatial Frequency (SF) demonstrates the superior quality and enhanced texture details achieved through the feature fusion approach. This proposed fusion technique not only enhances visual quality but also enriches the fused images with detailed information, highlighting its potential for various applications in image processing and analysis.