| Summary: | 碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 93 === One effective approach is to adapt face models using new data in new environment. This thesis presents a novel sequential learning algorithm for the support vector machine（SVM） based face recognition system. First of all, we use a aussian probability model to represent the randomness of SVM parameters. The recursive Bayes theory is applied to sequentially update a posteriori distribution of SVM parameters. The estimated mean vector is adopted to build the output distribution of SVM. During test phase, we classify a test image according to output distributions of two SVM classes. In this study, we demonstrate that the proposed sequential SVM can meet the standard properties of SVM, or equivalently, minimization of classification errors and maximization of distance of output distributions of two classes. In the experiments on using ORL and FERET facial databases, the proposed sequential SVM did improve face recognition accuracy when increasingly enrolling new face adaptation data.
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