TY - JOUR T1 - A Blur-robust Descriptor with Applications to Face Recognition JF - IEEE Transactions on Pattern Analysis and Machine Intelligence Y1 - 2012 A1 - Gopalan,R. A1 - Taheri, S. A1 - Turaga,P. A1 - Chellapa, Rama KW - Blur KW - convolution KW - Face KW - face recognition KW - Grassmann manifold KW - Kernel KW - Manifolds KW - NOISE KW - PROBES AB - 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. VL - PP SN - 0162-8828 CP - 99 M3 - 10.1109/TPAMI.2012.15 ER - TY - CONF T1 - Domain adaptation for object recognition: An unsupervised approach T2 - 2011 IEEE International Conference on Computer Vision (ICCV) Y1 - 2011 A1 - Gopalan,R. A1 - Ruonan Li A1 - Chellapa, Rama KW - Data models KW - data representations KW - discriminative classifier KW - Feature extraction KW - Grassmann manifold KW - image sampling KW - incremental learning KW - labeled source domain KW - Manifolds KW - measurement KW - object category KW - Object recognition KW - Principal component analysis KW - sampling points KW - semisupervised adaptation KW - target domain KW - underlying domain shift KW - unsupervised approach KW - unsupervised domain adaptation KW - Unsupervised learning KW - vectors AB - Adapting the classifier trained on a source domain to recognize instances from a new target domain is an important problem that is receiving recent attention. In this paper, we present one of the first studies on unsupervised domain adaptation in the context of object recognition, where we have labeled data only from the source domain (and therefore do not have correspondences between object categories across domains). Motivated by incremental learning, we create intermediate representations of data between the two domains by viewing the generative subspaces (of same dimension) created from these domains as points on the Grassmann manifold, and sampling points along the geodesic between them to obtain subspaces that provide a meaningful description of the underlying domain shift. We then obtain the projections of labeled source domain data onto these subspaces, from which a discriminative classifier is learnt to classify projected data from the target domain. We discuss extensions of our approach for semi-supervised adaptation, and for cases with multiple source and target domains, and report competitive results on standard datasets. JA - 2011 IEEE International Conference on Computer Vision (ICCV) PB - IEEE SN - 978-1-4577-1101-5 M3 - 10.1109/ICCV.2011.6126344 ER - TY - JOUR T1 - Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video-Based Recognition JF - IEEE Transactions on Pattern Analysis and Machine Intelligence Y1 - 2011 A1 - Turaga,P. A1 - Veeraraghavan,A. A1 - Srivastava, A. A1 - Chellapa, Rama KW - activity based video clustering KW - activity recognition KW - computational geometry KW - Computational modeling KW - Data models KW - face recognition KW - feature representation KW - finite dimensional linear subspaces KW - geometric properties KW - Geometry KW - Grassmann Manifolds KW - Grassmann. KW - HUMANS KW - Image and video models KW - image recognition KW - linear dynamic models KW - linear subspace structure KW - Manifolds KW - maximum likelihood classification KW - maximum likelihood estimation KW - Object recognition KW - Riemannian geometry KW - Riemannian metrics KW - SHAPE KW - statistical computations KW - statistical models KW - Stiefel KW - Stiefel Manifolds KW - unsupervised clustering KW - video based face recognition KW - video based recognition KW - video signal processing AB - In this paper, we examine image and video-based recognition applications where the underlying models have a special structure-the linear subspace structure. We discuss how commonly used parametric models for videos and image sets can be described using the unified framework of Grassmann and Stiefel manifolds. We first show that the parameters of linear dynamic models are finite-dimensional linear subspaces of appropriate dimensions. Unordered image sets as samples from a finite-dimensional linear subspace naturally fall under this framework. We show that an inference over subspaces can be naturally cast as an inference problem on the Grassmann manifold. To perform recognition using subspace-based models, we need tools from the Riemannian geometry of the Grassmann manifold. This involves a study of the geometric properties of the space, appropriate definitions of Riemannian metrics, and definition of geodesics. Further, we derive statistical modeling of inter and intraclass variations that respect the geometry of the space. We apply techniques such as intrinsic and extrinsic statistics to enable maximum-likelihood classification. We also provide algorithms for unsupervised clustering derived from the geometry of the manifold. Finally, we demonstrate the improved performance of these methods in a wide variety of vision applications such as activity recognition, video-based face recognition, object recognition from image sets, and activity-based video clustering. VL - 33 SN - 0162-8828 CP - 11 M3 - 10.1109/TPAMI.2011.52 ER - TY - CONF T1 - Towards view-invariant expression analysis using analytic shape manifolds T2 - 2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011) Y1 - 2011 A1 - Taheri, S. A1 - Turaga,P. A1 - Chellapa, Rama KW - Databases KW - Deformable models KW - Face KW - face recognition KW - facial expression analysis KW - Geometry KW - Gold KW - Human-computer interaction KW - Manifolds KW - projective transformation KW - Riemannian interpretation KW - SHAPE KW - view invariant expression analysis AB - Facial expression analysis is one of the important components for effective human-computer interaction. However, to develop robust and generalizable models for expression analysis one needs to break the dependence of the models on the choice of the coordinate frame of the camera i.e. expression models should generalize across facial poses. To perform this systematically, one needs to understand the space of observed images subject to projective transformations. However, since the projective shape-space is cumbersome to work with, we address this problem by deriving models for expressions on the affine shape-space as an approximation to the projective shape-space by using a Riemannian interpretation of deformations that facial expressions cause on different parts of the face. We use landmark configurations to represent facial deformations and exploit the fact that the affine shape-space can be studied using the Grassmann manifold. This representation enables us to perform various expression analysis and recognition algorithms without the need for the normalization as a preprocessing step. We extend some of the available approaches for expression analysis to the Grassmann manifold and experimentally show promising results, paving the way for a more general theory of view-invariant expression analysis. JA - 2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011) PB - IEEE SN - 978-1-4244-9140-7 M3 - 10.1109/FG.2011.5771415 ER -