@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}
}
@conference {12441,
	title = {Synthesis-based recognition of low resolution faces},
	booktitle = {2011 International Joint Conference on Biometrics (IJCB)},
	year = {2011},
	month = {2011/10/11/13},
	pages = {1 - 6},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Recognition of low resolution face images is a challenging problem in many practical face recognition systems. Methods have been proposed in the face recognition literature for the problem when the probe is of low resolution, and a high resolution gallery is available for recognition. These methods modify the probe image such that the resultant image provides better discrimination. We formulate the problem differently by leveraging the information available in the high resolution gallery image and propose a generative approach for classifying the probe image. An important feature of our algorithm is that it can handle resolution changes along with illumination variations. The effective- ness of the proposed method is demonstrated using standard datasets and a challenging outdoor face dataset. It is shown that our method is efficient and can perform significantly better than many competitive low resolution face recognition algorithms.},
	keywords = {Dictionaries, Face, face images, face recognition, face recognition literature, face recognition systems, illumination variations, image resolution, low resolution faces, Organizations, PROBES, support vector machines, synthesis based recognition},
	isbn = {978-1-4577-1358-3},
	doi = {10.1109/IJCB.2011.6117545},
	author = {Shekhar, S. and Patel, Vishal M. and Chellapa, Rama}
}
@conference {15856,
	title = {Replication and automation of expert judgments: Information engineering in legal e-discovery},
	booktitle = {IEEE International Conference on Systems, Man and Cybernetics, 2009. SMC 2009},
	year = {2009},
	month = {2009/10/11/14},
	pages = {102 - 107},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The retrieval of digital evidence responsive to discovery requests in civil litigation, known in the United States as {\~A}{\^A}{\textquestiondown}e-discovery,{\~A}{\^A}{\textquestiondown} presents several important and understudied conditions and challenges. Among the most important of these are (i) that the definition of responsiveness that governs the search effort can be learned and made explicit through effective interaction with the responding party, (ii) that the governing definition of responsiveness is generally complex, deriving both from considerations of subject-matter relevance and from considerations of litigation strategy, and (iii) that the result of the search effort is a set (rather than a ranked list) of documents, and sometimes a quite large set, that is turned over to the requesting party and that the responding party certifies to be an accurate and complete response to the request. This paper describes the design of an {\~A}{\^A}{\textquestiondown}interactive task{\~A}{\^A}{\textquestiondown} for the text retrieval conference{\textquoteright}s legal track that had the evaluation of the effectiveness of e-discovery applications at the {\~A}{\^A}{\textquestiondown}responsive review{\~A}{\^A}{\textquestiondown} task as its goal. Notable features of the 2008 interactive task were high-fidelity human-system task modeling, authority control for the definition of {\~A}{\^A}{\textquestiondown}responsiveness,{\~A}{\^A}{\textquestiondown} and relatively deep sampling for estimation of type 1 and type 2 errors (expressed as {\~A}{\^A}{\textquestiondown}precision{\~A}{\^A}{\textquestiondown} and {\~A}{\^A}{\textquestiondown}recall{\~A}{\^A}{\textquestiondown}). The paper presents a critical assessment of the strengths and weaknesses of the evaluation design from the perspectives of reliability, reusability, and cost-benefit tradeoffs.},
	keywords = {authorisation, authority control, Automation, civil litigation, CYBERNETICS, Delay, digital evidence retrieval, discovery request, Educational institutions, expert judgment automation, Human computer interaction, Human-machine cooperation and systems, human-system task modeling, information engineering, Information retrieval, interactive task, Law, law administration, legal e-discovery, Legal factors, PROBES, Production, Protocols, search effort, Search methods, text analysis, text retrieval conference legal track, United States, USA Councils, User modeling},
	isbn = {978-1-4244-2793-2},
	doi = {10.1109/ICSMC.2009.5346118},
	author = {Hedin,B. and Oard, Douglas}
}
@conference {16503,
	title = {Web Monitoring 2.0: Crossing Streams to Satisfy Complex Data Needs},
	booktitle = {IEEE 25th International Conference on Data Engineering, 2009. ICDE {\textquoteright}09},
	year = {2009},
	month = {2009/04/29/March},
	pages = {1215 - 1218},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Web monitoring 2.0 supports the complex information needs of clients who probe multiple information sources and generate mashups by integrating across these volatile streams. A proxy that aims at satisfying multiple customized client profiles will face a scalability challenge in trying to maximize the number of clients served while at the same time fully satisfying complex client needs. In this paper, we introduce an abstraction of complex execution intervals, a combination of time intervals and information streams, to capture complex client needs. Given some budgetary constraints (e.g., bandwidth), we present offline algorithmic solutions for the problem of maximizing completeness of capturing complex profiles.},
	keywords = {Bandwidth, complex client information need, Data Delivery, Data engineering, database management systems, Educational institutions, Internet, Mashups, mashups generation, Monitoring, multiple information source, offline algorithmic solution, Portals, PROBES, Profiles, Query processing, scalability, scheduling, volatile information stream, Web 2.0, Web Monitoring},
	isbn = {978-1-4244-3422-0},
	doi = {10.1109/ICDE.2009.204},
	author = {Roitman,H. and Gal,A. and Raschid, Louiqa}
}
