SVM-Based Face Recognition System

This is a MATLAB-implemented face recognition system based on Support Vector Machine (SVM). The system employs SVM algorithm to achieve efficient recognition and classification of facial images. SVM is a powerful machine learning algorithm widely applied in pattern recognition and image processing fields. The implementation includes key components such as: - Feature extraction using techniques like HOG (Histogram of Oriented Gradients) or LBP (Local Binary Patterns) - Multi-class SVM classification with either one-vs-one or one-vs-all strategies - Kernel function optimization (linear, polynomial, or RBF kernels) for better separation 1.Preprocessing module for image normalization and alignment Through careful debugging and optimization, this system has demonstrated excellent performance in practical applications. It provides reliable face recognition and verification capabilities suitable for various domains including security surveillance, facial unlock systems, and facial payment authentication. The system architecture implements: - Automated training pipeline with cross-validation - Real-time recognition interface - Performance evaluation metrics (accuracy, precision, recall) This SVM-based face recognition system offers convenient and valuable solutions for biometric authentication applications, featuring robust implementation and proven effectiveness in real-world scenarios.