@article {12433,
	title = {A Blur-robust Descriptor with Applications to Face Recognition},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {PP},
	year = {2012},
	month = {2012/01/10/},
	pages = {1 - 1},
	abstract = {Understanding the effect of blur is an important problem in unconstrained visual analysis. We address this problem in the context of image-based recognition, by a fusion of image-formation models, and differential geometric tools. First, we discuss the space spanned by blurred versions of an image and then under certain assumptions, provide a differential geometric analysis of that space. More specifically, we create a subspace resulting from convolution of an image with a complete set of orthonormal basis functions of a pre-specified maximum size (that can represent an arbitrary blur kernel within that size), and show that the corresponding subspaces created from a clean image and its blurred versions are equal under the ideal case of zero noise, and some assumptions on the properties of blur kernels. We then study the practical utility of this subspace representation for the problem of direct recognition of blurred faces, by viewing the subspaces as points on the Grassmann manifold and present methods to perform recognition for cases where the blur is both homogenous and spatially varying. We empirically analyze the effect of noise, as well as the presence of other facial variations between the gallery and probe images, and provide comparisons with existing approaches on standard datasets.},
	keywords = {Blur, convolution, Face, face recognition, Grassmann manifold, Kernel, Manifolds, NOISE, PROBES},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2012.15},
	author = {Gopalan,R. and Taheri, S. and Turaga,P. and Chellapa, Rama}
}
@article {12434,
	title = {Remote identification of faces: Problems, prospects, and progress},
	journal = {Pattern Recognition Letters},
	year = {2011},
	month = {2011/12//},
	abstract = {Face recognition in unconstrained acquisition conditions is one of the most challenging problems that has been actively researched in recent years. It is well known that many state-of-the-art still face recognition algorithms perform well, when constrained (frontal, well illuminated, high-resolution, sharp, and full) face images are acquired. However, their performance degrades significantly when the test images contain variations that are not present in the training images. In this paper, we highlight some of the key issues in remote face recognition. We define the remote face recognition as one where faces are several tens of meters (10{\textendash}250\&$\#$xa0;m) from the cameras. We then describe a remote face database which has been acquired in an unconstrained outdoor maritime environment. Recognition performance of a subset of existing still image-based face recognition algorithms is evaluated on the remote face data set. Further, we define the remote re-identification problem as matching a subject at one location with candidate sets acquired at a different location and over time in remote conditions. We provide preliminary experimental results on remote re-identification. It is demonstrated that in addition to applying a good classification algorithm, finding features that are robust to variations mentioned above and developing statistical models which can account for these variations are very important for remote face recognition.},
	keywords = {Blur, illumination, low-resolution, pose variation, Re-identification, Remote face recognition},
	isbn = {0167-8655},
	doi = {10.1016/j.patrec.2011.11.020},
	url = {http://www.sciencedirect.com/science/article/pii/S0167865511004107},
	author = {Chellapa, Rama and Ni,Jie and Patel, Vishal M.}
}
