Hidden Markov Model (HMM) for Face Recognition

In this document, I share a MATLAB program for face recognition based on Hidden Markov Models (HMM). This robust implementation successfully runs and provides significant insights into understanding the HMM algorithm. The program operates through sequential stages: preprocessing facial images, extracting features like Discrete Cosine Transform (DCT) coefficients, training HMMs using the Baum-Welch algorithm for each subject, and classifying new images via the Viterbi algorithm for maximum likelihood estimation. By analyzing facial image features, the system enables accurate face identification with strong potential for security and authentication applications. The code structure includes modular functions for data loading, model initialization, and performance evaluation, making it suitable for research and development in face recognition technology. It helps users deepen their understanding of HMM mechanics through hands-on experimentation with real-world image processing workflows. If you are interested in pattern recognition algorithms, I highly recommend experimenting with this MATLAB program—it demonstrates practical implementation of probabilistic modeling for biometric systems and offers valuable learning opportunities through customizable parameters and visualizable intermediate results.