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
Resolution enhancement of facial image based on top-down learning
IWVS '03 First ACM SIGMM international workshop on Video surveillance
IEEE Transactions on Image Processing
Resolution enhancement of monochrome and color video using motion compensation
IEEE Transactions on Image Processing
Mesh resolution augmentation using 3D skin bank
Computer-Aided Design
Region-based reconstruction for face hallucination
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Stepwise reconstruction of high-resolution facial image based on interpolated morphable face model
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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This paper proposes a new method of enhancing the resolution of facial image from a low-resolution facial image using a recursive error back-projection of example-based 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 obtained by using the optimal coefficients for linear combination of the high-resolution prototypes. In addition to, a recursive error back-projection is applied to improve the accuracy of resolution enhancement. The encouraging results of the proposed method show that our method can be used to improve the performance of the face recognition by applying our method to enhance the low-resolution facial images captured at visual surveillance systems.