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Wide area surveillance situations require many sensors, thus making the use of highresolution cameras prohibitive because of high costs and exponential growth in storage. Small and low cost CCTV cameras may produce poor quality video, and high-resolution CCD cameras in wide area surveillance can still yield low-resolution images of the object of interest, due to large distances from the camera. All these restrictions and limitations pose problems for subsequent tasks such as face recognition or vehicle registration plate recognition. Super-Resolution (SR) offers a way to increase the resolution of such images or videos and is well studied in the last decades (Farsiu et al., 2004; Park et al., 2003; Baker & Kanade 2002). However, most existing SR algorithms are not suitable for video sequences of faces because a face is a non-planar and non-rigid object, violating the underlying assumptions of many SR algorithms (Baker & Kanade, 1999). A common SR algorithm is the super-resolution optical flow (Baker & Kanade, 1999). Each frame is interpolated to twice its size and optical flow is used to register previous and consecutive frames, which are then warped into a reference coordinate system. The superresolved image is calculated as the average across these warped frames. However, the first step of interpolation introduces artificial random noise which is difficult to remove. Secondly, the optical flow is calculated between previous and consecutive frames preventing its use as an online stream processing algorithm. Also, accurate image registration requires precise motion estimation (Barreto et al., 2005) which in turn affects the quality of the super-resolved image as reported in (Zhao & Sawhney, 2002). Optical flow in general fails in low textured areas and causes problems in registering non-planar and nonrigid objects in particular. Recent techniques like (Gautama & van Hulle, 2002) calculate subpixel optical flow between several consecutive frames (with non-planar and non-rigid moving objects) however they are unable to estimate an accurate dense flow field, which is needed for accurate image warping. Although solving all the issues in a general case is difficult, as the general problem of superresolution is numerically ill-posed and computationally complex (Farsiu et al., 2004), we address a specific issue: Simultaneous tracking and increasing super-resolution of known object types, in our case faces, acquired by low resolution video. The use of an object-specific 3D mesh overcomes the issues with optic flow failures in low textured images. We avoid the O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

A Model-based Approach for Combined Tracking and Resolution Enhancement of Faces in Low Resolution Video