Active shape models—their training and application
Computer Vision and Image Understanding
Face Hallucination: Theory and Practice
International Journal of Computer Vision
Hallucinating face by eigentransformation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Eigenface-domain super-resolution for face recognition
IEEE Transactions on Image Processing
Generalized Face Super-Resolution
IEEE Transactions on Image Processing
An Example-Based Face Hallucination Method for Single-Frame, Low-Resolution Facial Images
IEEE Transactions on Image Processing
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In real surveillance scenarios, a variety of factors have an impact on the quality of images, which leads to pixel distortion and aliasing. Traditional face super-resolution algorithms only use the difference of image pixel values as similarity criterion, which degrades similarity and identification of reconstructed facial images. Image semantic information with human understanding, especially structural data of shapes, is robust to the degraded images. In this paper, we propose a face hallucination with shape parameters projection constraint. This method uses a parameter model to represent face shapes, and shape information of input image is introduced to improving the quality of reconstructed image. The shape model regularization is first added to original objective function. Then shape parameters are projected into the domain of image parameters by a linear regression model. Finally, the gradient descent method is used to obtain the unified parameters. Experimental results demonstrate the proposed method outperforms the traditional schemes significantly both in subjective and objective quality.