Active shape models—their training and application
Computer Vision and Image Understanding
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Active Appearance Models Revisited
International Journal of Computer Vision
Probability and Random Processes For EE's (3rd Edition)
Probability and Random Processes For EE's (3rd Edition)
The Asymmetry of Image Registration and Its Application to Face Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Hallucination: Theory and Practice
International Journal of Computer Vision
Journal of Cognitive Neuroscience
International Journal of Computer Vision
Image super-resolution via sparse representation
IEEE Transactions on Image Processing
Efficient graphical models for processing images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Resolution-Aware fitting of active appearance models to low resolution images
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
A regularized iterative image restoration algorithm
IEEE Transactions on Signal Processing
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
High resolution image formation from low resolution frames using Delaunay triangulation
IEEE Transactions on Image Processing
Eigenface-domain super-resolution for face recognition
IEEE Transactions on Image Processing
A generalized Gaussian image model for edge-preserving MAP estimation
IEEE Transactions on Image Processing
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We present a computationally efficient method for the super-resolution reconstruction of face images from their low-resolution versions. It is based on generative models and utilizes both the shape and texture components together. The main idea is that the image details can be synthesized by global modeling of accurately aligned local image regions. In order to achieve sufficient accuracy in alignment, shape reconstruction is considered as a separate problem and solved together with texture reconstruction in a coordinated manner. Meanwhile, the statistical dependency between the shape and texture components is also considered. Moreover, different from traditional model-based super-resolution methods, we use a corrected form of the degradation operator with the aligned images. We show that when the degradation is used with the aligned texture components as is, it causes bias in the reconstructions. To overcome this problem, we reflect the same processing performed in alignment onto the degradation operator and use this corrected version in texture reconstruction. Experimental results show that the proposed solution provides superior image reconstructions (both qualitatively and quantitatively) in a faster way.