Face recognition system based on Doubly truncated multivariate Gaussian Mixture Model
نویسندگان
چکیده
A face recognition algorithm based on doubly truncated multivariate Gaussian mixture model with DCT is introduced. The truncation on the feature vector with a significant influence on improving the recognition rate of the system using EM algorithm with K-means or hierarchical clustering is implemented. The characteristic model parameters are estimated. The EM algorithm containing the updated equations of the model parameters derived for the doubly truncated multivariate Gaussian mixture model. A face recognition system is developed under Bayesian frame using maximum likelihood conditions. The efficiency of the developed face recognition system is analyzed by conducting experimentation with two face image databases, via, of Jawaharlal Nehru Technological University Kakinada (JNTUK) and Yale. The performance of these algorithms are evaluated by computing the recognition rates, false acceptance rate, false rejection rate, true positive rate and half error rate. From the ROC curves, it is observed the developed models perform better. A comparative study of the present face recognition systems with that of the face recognition systems based on Gaussian mixture models reveal that the proposed algorithms perform better.
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