MATLAB Implementation of Face Recognition with PCA Algorithm

After comprehending the PCA algorithm, I developed a MATLAB GUI-based face recognition system. This project applies PCA algorithm to image processing by reducing dimensionality of sample images, enabling efficient face recognition through eigenface computation and feature matching. The GUI operation follows these steps: 1. Execute the main script face.m to initialize the GUI environment and load necessary libraries for image processing and matrix operations 2. Click "Train Machine" to select a folder containing training images - the system processes these images to create a feature space using covariance matrix calculation and eigenvalue decomposition 3. Click "Choose Photo" to select a single test image from the test folder - the interface supports common image formats and performs preprocessing including normalization and vectorization 4. Click "Recognize" to perform identification - the system projects the test image onto the PCA subspace and computes Euclidean distance for classification against trained features 5. Click "Accuracy" to calculate recognition accuracy across all images in the test folder - this implements batch processing with statistical analysis of match rates This project demonstrates practical application of PCA algorithm in computer vision and utilizes MATLAB's GUI components for interactive image processing. The implementation includes key functions for matrix operations, statistical analysis, and real-time image display capabilities, providing a foundation for further research in biometric systems and pattern recognition applications.