Exploit more information of the sample for representation based face recognition
Kezong Tang, Xuan Xiao, Zuoyong Li · International Journal of Wireless and Mobile Computing · 2015
Besides the representation based classification method defined in the original space for face recognition, the method has also been extended to the feature space. As defined in the original space the method uses a linear combination of all the training samples to approximate the test sample in the original space and then exploits the determined linear combination of all the training samples of each class to classify the test sample. As defined in the feature space the method first implicitly transforms the sample into a high-dimensional space, then classify the test sample in the feature space. In this paper, we propose to integrate the original data of the sample and its representation in the feature space for face recognition based on their respective rationales. The proposed method outperforms the representation based classification method defined in the original space.