Face Recognition Using Support Vector Machines (SVM)

This MATLAB source code implements face recognition using Support Vector Machine (SVM) algorithm. The code is organized into five key components: 1. ORL Face Database: Contains face images for training and testing the SVM model. The database preprocessing typically involves image normalization and feature extraction before feeding into the classifier. 2. OSU_SVM Toolbox: Provides essential SVM functions for model training and prediction. Key functions include svm_train() for creating the classifier and svm_classify() for making predictions on new face images. 3. Main Program (my program): The core implementation with comprehensive comments explaining the workflow. The code demonstrates complete SVM pipeline including data loading, feature selection, model training, cross-validation, and performance evaluation. 4. Word Analysis Report: Contains detailed results analysis covering accuracy metrics, confusion matrices, and performance comparisons. The report helps evaluate the SVM model's effectiveness in face recognition tasks. 5. Usage Instructions: Important guidelines for proper code execution, including environment setup, dependency requirements, and parameter configuration tips to ensure optimal performance. This implementation showcases practical application of SVM for computer vision tasks, featuring proper machine learning workflow and evaluation methodologies. Download and explore the code to understand SVM-based pattern recognition techniques!