Face Recognition System Combining Markov Model and Support Vector Machine

This documentation presents a face recognition system integrating Markov Model and Support Vector Machine (SVM), implemented in MATLAB with a comprehensive face database. Although not an original creation, the system demonstrates exceptional performance and powerful capabilities through its well-structured code architecture. The implementation includes dynamic database generation from training and testing samples, enhancing recognition accuracy through iterative model optimization. The core algorithm employs Markov chains for facial feature sequence analysis and SVM with kernel functions for classification decision boundaries. Additionally, the system supports real-time face recognition from camera-captured images, utilizing image preprocessing techniques and frame-by-frame analysis. The complete pipeline involves face detection, feature vector extraction using state transition probabilities, and multi-class SVM classification. This practical and powerful system finds applications in security surveillance, biometric authentication, and facial recognition payment systems, featuring modular code design for easy customization and expansion.