Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reconstruction of Partially Damaged Face Images Based on a Morphable Face Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Identification across Different Poses and Illuminations with a 3D Morphable Model
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Example Based Image Analysis and Synthesis
Example Based Image Analysis and Synthesis
IEEE Transactions on Image Processing
Resolution enhancement of monochrome and color video using motion compensation
IEEE Transactions on Image Processing
Resolution enhancement of facial image using an error back-projection of example-based learning
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A Comprehensive Survey to Face Hallucination
International Journal of Computer Vision
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This paper proposes a new method of synthesizing a highresolution facial image from a low-resolution facial image based on top-down learning. A face is represented by a linear combination of prototypes of shape and texture. With the shape and texture information about the pixels in a given low-resolution facial image, we can estimate optimal coefficients for a linear combination of prototypes of shape and those of texture by solving least square minimization. Then high-resolution facial image can be synthesized by using the optimal coeffcients for linear combination of the high-resolution prototypes.The encouraging results of the proposed method show that our method can be used to increase the performance of the face recognition by applying our method to enhance the low-resolution facial images captured at surveillance systems.