Face Recognition System Based on Hidden Markov Model

This face recognition system leverages Hidden Markov Models (HMM) with comprehensive technical implementations including feature extraction algorithms and classifier design. The primary objective is facial identification for authentication purposes or security applications. The system architecture incorporates complete testing protocols to validate reliability and accuracy, featuring simulated real-world datasets that account for variations in lighting conditions and facial expressions. From an implementation perspective, the system employs dimensional reduction techniques for feature extraction and implements Baum-Welch algorithm for HMM parameter estimation. The testing framework includes cross-validation procedures and performance metrics calculation to ensure robust recognition capabilities. The modular design allows for seamless integration of new facial datasets through retraining workflows, enhancing the system's adaptability. In summary, this HMM-based facial recognition solution demonstrates reliable performance, high accuracy, and scalable architecture suitable for diverse applications including security systems and identity verification platforms.