Super-resolution of faces using texture mapping on a generic 3D model

This paper proposes a novel face texture mapping framework to transform faces with different poses into a unique texture map. Under this framework, texture mapping can be realized by utilizing a generic 3D face model, standard Haar-like feature based detector, active appearance model and pose estimation algorithm. By this texture map, correspondence of every pixel at the face across multiple distinct input images can then be established, which enables super-resolution algorithms to be applied directly on registered texture map to render high resolution faces. This paper details the proposed framework, and illustrates how the proposed super-resolution algorithm works with the help of weighted average and median filters. Convincing experimental results are also presented to validate the effectiveness of the proposed framework and superresolution algorithm. © 2009 IEEE.published_or_final_versio

Super-resolution of faces using texture mapping on a generic 3D model

This paper proposes a novel face texture mapping framework to transform faces with different poses into a unique texture map. Under this framework, texture mapping can be realized by utilizing a generic 3D face model, standard Haar-like feature based detector, active appearance model and pose estimation algorithm. By this texture map, correspondence of every pixel at the face across multiple distinct input images can then be established, which enables super-resolution algorithms to be applied directly on registered texture map to render high resolution faces. This paper details the proposed framework, and illustrates how the proposed super-resolution algorithm works with the help of weighted average and median filters. Convincing experimental results are also presented to validate the effectiveness of the proposed framework and superresolution algorithm. © 2009 IEEE.published_or_final_versio

Super-resolution of faces using the epipolar constraint

In this paper we present a super-resolution scheme specifically designed for faces. First, a face detector is used to find faces in a video frame, after which an optical flow algorithm is applied to track feature points on the faces. Given the set of flow vectors corresponding to a single face, we propose to use the epipolar geometry for rejecting outlying flow vectors. This will improve the registration of the face over multiple frames, and thus lead to an improved super-resolution image. An iterative backprojection method is used for acquiring the super-resolution image