MATLAB Program for Face Recognition

This documentation discusses a MATLAB implementation for face recognition. The program follows a two-phase workflow: initial training followed by testing. When an input image is provided, the system outputs several face images from the dataset that most closely match the input. Face recognition represents a critical technology with significant applications in security systems, human-computer interaction, and numerous other domains. Using MATLAB's computational capabilities and image processing toolbox, we can efficiently implement face recognition functionality for various practical scenarios. The implementation typically involves preprocessing steps like face detection and normalization, followed by feature extraction using algorithms such as PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), or deep learning approaches. The system then performs similarity comparison using distance metrics like Euclidean or Cosine distance. Through MATLAB's programming environment, developers can experiment with different algorithms and techniques to improve recognition accuracy and computational efficiency. Key functions commonly used include imread for image loading, vision.CascadeObjectDetector for face detection, and pca for dimensionality reduction. The program structure typically involves separate modules for dataset preprocessing, model training, and live testing. Thus, this MATLAB face recognition program serves as both a practical tool and educational resource for understanding and applying computer vision technologies.