Face Recognition Using Hidden Markov Models (HMM)

We have developed a face recognition program based on Hidden Markov Models (HMM) using MATLAB implementation. The program extracts facial recognition features specifically using Discrete Cosine Transform (DCT) coefficients, where the algorithm processes input images through DCT transformation to capture essential facial patterns in frequency domains. By analyzing these feature vectors, our system achieves accurate face identification through HMM's probabilistic state transitions that model facial regions as sequential observation sequences. During experimental validation, we employed extensive face image datasets for both training and testing phases, ensuring program accuracy and robustness through cross-validation techniques. The implementation includes performance optimizations such as vectorized DCT computations and efficient Baum-Welch algorithm for HMM parameter estimation, significantly improving processing speed and computational efficiency. The core MATLAB functions involve: - dct2() for 2D discrete cosine transform feature extraction - hmmtrain() for model parameter learning - hmmdecode() for recognition inference - Custom preprocessing routines for image normalization Overall, our HMM-based face recognition system represents a powerful and reliable tool applicable across various domains including security surveillance, access control systems, and biometric authentication, with demonstrated effectiveness in handling facial variations and lighting conditions.