PPT-Extended Sparse Linear Model for Face Recognition

Weihong Deng 邓伟洪 Beijing Univ Post amp 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. e Ax where is vector is a linear function of ie By where is then is a linear function of and By BA so matrix multiplication corresponds to composition of linear functions ie linear functions of linear functions of some variables Linear Equations Aswin C Sankaranarayanan. Rice University. Richard G. . Baraniuk. Andrew E. Waters. Background subtraction in surveillance videos. s. tatic camera with foreground objects. r. ank 1 . background. s. parse. Richard Peng. M.I.T.. Joint work with Dan Spielman (Yale). Efficient Parallel Solvers for SDD Linear Systems. Richard Peng. M.I.T.. Work in progress with . Dehua. Cheng (USC),. Yu Cheng (USC), . Yintat. onto convex sets. Volkan. Cevher. Laboratory. for Information . . and Inference Systems – . LIONS / EPFL. http://lions.epfl.ch . . joint work with . Stephen Becker. Anastasios. . Kyrillidis. ISMP’12. . hongliang. . xue. Motivation. . Face recognition technology is widely used in our lives. . Using MATLAB. . ORL database. Database. The ORL Database of Faces. taken between April 1992 and April 1994 at the Cambridge University Computer . Sabareesh Ganapathy. Manav Garg. Prasanna. . Venkatesh. Srinivasan. Convolutional Neural Network. State of the art in Image classification. Terminology – Feature Maps, Weights. Layers - Convolution, . 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. Author: . Vikas. . Sindhwani. and . Amol. . Ghoting. Presenter: . Jinze. Li. Problem Introduction. we are given a collection of N data points or signals in a high-dimensional space R. D. : xi ∈ . 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. Michael A. Heroux. Director of Software Technology, Exascale Computing Project. Senior Scientist, Sandia National Laboratories. Numerical algorithms for . highperformance. computational science. London, UK. Linda Shapiro. ECE P 596. 1. What’s Coming. Review of . Bakic. flesh . d. etector. Fleck and Forsyth flesh . d. etector. Review of Rowley face . d. etector. Overview of. . Viola Jones face detector with . Afsaneh . Asaei. Joint work with: . Mohammad . Golbabaee. ,. Herve. Bourlard, . Volkan. . Cevher. φ. 21. φ. 52. s. 1. s. 2. s. 3. . s. 4. s. 5. x. 1. x. 2. φ. 11. φ. 42. 2. Speech . Separation Problem. Jim . Demmel. EECS & Math Departments. UC Berkeley. Why avoid communication? . Communication = moving data. Between level of memory hierarchy. Between processors over a network. Running time of an algorithm is sum of 3 terms:.