PDF-Robust Face Recognition via Sparse Representation John Wright Student Member IEE

Yang Member IEEE Arvind Ganesh Student Member IEEE S Shankar Sastry Fellow IEEE and Yi Ma Senior Member IEEE Abstract We consider the problem of automatically recognizing human faces from frontal views with varying expression and illuminationaswel. The face image is divided into several regions from which the LBP feature distributions are extrac ted and concatenated into an enhanced feature vector to be used as a face descriptor Th e performance of the proposed method is assessed in the face r :. A Literature Survey. By:. W. Zhao, R. Chellappa, P.J. Phillips,. and A. Rosenfeld. Presented By:. Diego Velasquez. Contents . Introduction. Why do we need face recognition?. Biometrics. Face Recognition by Humans. J. Friedman, T. Hastie, R. . Tibshirani. Biostatistics, 2008. Presented by . Minhua. Chen. 1. Motivation. Mathematical Model. Mathematical Tools. Graphical LASSO. Related papers. 2. Outline. Motivation. Aditya. Chopra and Prof. Brian L. Evans. Department of Electrical and Computer Engineering. The University of Texas at Austin. 1. Introduction. Finite Impulse Response (FIR) model of transmission media. . Michael Elad. The Computer Science Department. The Technion – Israel Institute of technology. Haifa 32000, Israel. MS45: Recent Advances in Sparse and . Non-local Image Regularization - Part III of III. Weihong Deng (. 邓伟洪. ). Beijing Univ. Post. & Telecom.(. 北京邮电大学. ) . 2. Characteristics of Face Pattern. The facial shapes are too similar, sometimes identical ! (~100% face detection rate, kinship verification). Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . Chao . Jia. and Brian L. Evans. The University of Texas at Austin. 12 Sep 2011. 1. Non-blind Image Deconvolution. Reconstruct natural image from blurred version. Camera shake; astronomy; biomedical image reconstruction. Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. Object Recognition. Murad Megjhani. MATH : 6397. 1. Agenda. Sparse Coding. Dictionary Learning. Problem Formulation (Kernel). Results and Discussions. 2. Motivation. Given a 16x16(or . nxn. ) image . Parallelization of Sparse Coding & Dictionary Learning Univeristy of Colorado Denver Parallel Distributed System Fall 2016 Huynh Manh 11/15/2016 1 Contents Introduction to Sparse Coding Applications of Sparse Representation Linda Shapiro. CSE 455. 1. Face recognition: once you’ve detected and cropped a face, try to recognize it. Detection. Recognition. “Sally”. 2. Face recognition: overview. Typical scenario: few examples per face, identify or verify test example. Hao Zhang. Computer Science Department. 1. Problem Statement. Verification. Identification. A. B. Same / Different persons?. A. B. C. D. Which has the same identity as A?. 2. Solutions. Extensions of still face recognition algorithms. Adeetya's Kitchen & Furniture is an innovative brand that specializes in providing high-quality best Modular Kitchen Systems and Furniture to customers. https://adeetyas.com/