Real-time Face Recognition Algorithm Using Hidden Markov Model Based on Complex Feature Extraction
An algorithm of face recognition based on Hidden Markov Model (HMM for abbreviation) is presented here.First,the difference algorithm was used to analyze each of the frames of the picture captured from camera in a real time and to position the region of the face shortly.Then,the data of the region of the face was normalized.DCT and gray transforms were applied to the raw picture,and the transformed data used as eigenvectors were clustered.Next,the clustered eigenvectors were the vector of observation of HMM.The samples were trained and the results were saved as the face recognition template.Finally,the picture of face captured in real time was recognized by the templates.The experimental results show that the algorithm identifies the picture of face with great efficiency and speed.