The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
New edge-directed interpolation
IEEE Transactions on Image Processing
Advances in virtual learning environments and classrooms
Proceedings of the 14th Communications and Networking Symposium
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Face recognition systems which perform in unconstrained environment needs to cope with vary input resolutions. Low resolution input images are often quite problematic. This paper describes the improvement obtained on facial expression recognition and identity recognition when applying resolution upscaling methods based on trained filter techniques. In this paper, we explore the method proposed by Kondo et al. Kondo's methode has already been applied in display technology successfully, but hasn't been studied for recognition tasks before. In addition, we examine the influence on the recognition performance of the type of material exploited to train the upscaling filters, by selecting both generic videos and face images as samples. We then analyze how the size of the training set used in generating the upscaling filters influence the recognition rates. We show that adopting faces as training material produces upscaling filters capable of both higher recognition performance and much reduced requirements on the training size.