| Summary: | 碩士 === 國立高雄應用科技大學 === 資訊工程系 === 99 === This study proposed a discriminant coupled subspace to deal with low-resolution face image set recognition problem. Compared to the traditional super-resolution method, It need a preprocess to synthesis of high-resolution images set from low-resolution images before identification procedures, It through the joint sub-space design in this letter, construct high-resolution set and low resolution face image set features of the relationship and solve the traditional method, due to synthesis of high-resolution face images time-consuming problem. In the joint sub-space of discriminant , the goal is to make the training data of high-resolution image set and low-resolution image set with the highest degree of similarity. In addition, low-resolution images due to loss of face images in high-frequency information, which enables high resolution include more relationship information to reduce of identification errors. Thus, in the sub-space design, the relationship further through the data between the minimum of false positives , making the learning subspace with better discernment. It using Yale B face database and Honda UCSD Video database to verify the correctness of the method in the Experiment.
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