MATLAB Implementation of Face Recognition with Illumination Normalization Algorithm

The program developed in MATLAB enables face recognition capabilities while incorporating illumination normalization algorithms. Face recognition represents a computer vision methodology that identifies individuals by processing and comparing facial images to determine identity. The illumination normalization algorithm addresses the impact of lighting variations on recognition performance by performing light compensation on images, enabling better matching and comparison of facial images captured under different lighting conditions. Key implementation aspects include: - Utilizing MATLAB's Image Processing Toolbox for facial feature extraction - Implementing histogram equalization or Retinex-based approaches for illumination normalization - Applying Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA) for dimensionality reduction and feature classification - Creating database templates through eigenface or fisherface methodologies By leveraging MATLAB's computational capabilities, this implementation achieves robust face recognition while employing illumination normalization techniques to significantly improve recognition accuracy and system stability across diverse lighting environments. The code structure typically involves preprocessing stages for image enhancement, feature extraction modules, and classification components using machine learning algorithms.