Zhiming Liu and Chengjun Liu, IEEE. Introduction Algorithms and procedures Experiments Conclusion.

Slides:

Advertisements

Similar presentations

Principal Component Analysis Based on L1-Norm Maximization Nojun Kwak IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008.

Advertisements

Parallel Fast Fourier Transform Ryan Liu. Introduction The Discrete Fourier Transform could be applied in science and engineering. Examples: ◦ Voice recognition.

Carolina Galleguillos, Brian McFee, Serge Belongie, Gert Lanckriet Computer Science and Engineering Department Electrical and Computer Engineering Department.

Face Recognition By Sunny Tang.

吳育德陽明大學放射醫學科學研究所台北榮總整合性腦功能研究室 Introduction To Linear Discriminant Analysis.

As applied to face recognition.  Detection vs. Recognition.

Kernelized Discriminant Analysis and Adaptive Methods for Discriminant Analysis Haesun Park Georgia Institute of Technology, Atlanta, GA, USA (joint work.

Face Recognition Committee Machine Presented by Sunny Tang.

Face Recognition Under Varying Illumination Erald VUÇINI Vienna University of Technology Muhittin GÖKMEN Istanbul Technical University Eduard GRÖLLER Vienna.

March 15-17, 2002Work with student Jong Oh Davi Geiger, Courant Institute, NYU On-Line Handwriting Recognition Transducer device (digitizer) Input: sequence.

CS 790Q Biometrics Face Recognition Using Dimensionality Reduction PCA and LDA M. Turk, A. Pentland, "Eigenfaces for Recognition", Journal of Cognitive.

1 Extracting Discriminative Binary Template for Face Template Protection Feng Yicheng Supervisor: Prof. Yuen August 31 st, 2009.

Eigenfaces As we discussed last time, we can reduce the computation by dimension reduction using PCA –Suppose we have a set of N images and there are c.

Face Recognition using PCA (Eigenfaces) and LDA (Fisherfaces)

Feature Extraction for Outlier Detection in High- Dimensional Spaces Hoang Vu Nguyen Vivekanand Gopalkrishnan.

12009/12/16 Hierarchical Ensemble of Global and Local Classifiers for Face Recognition Yu Su, Shiguang Shan, Member, IEEE, Xilin Chen, Member, IEEE, and.

A Hybrid Color and Frequency Features Method for Face Recognition 程式開發：賴博文報告者：邱威智 ISMP 郭淑美老師實驗室成員：賴博文鄭鈺勳邱威智.

PCA Channel Student: Fangming JI u Supervisor: Professor Tom Geoden.

Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP.

Comparing Kernel-based Learning Methods for Face Recognition Zhiguo Li

Face Recognition: An Introduction

Input image Output image Transform equation All pixels Transform equation.

Foundation of High-Dimensional Data Visualization

Dimensionality reduction Usman Roshan CS 675. Supervised dim reduction: Linear discriminant analysis Fisher linear discriminant: –Maximize ratio of difference.

Recognition Part II Ali Farhadi CSE 455.

Presented By Wanchen Lu 2/25/2013

Face Recognition and Feature Subspaces

Lecture 19 Representation and description II

Face Recognition and Feature Subspaces

1 Graph Embedding (GE) & Marginal Fisher Analysis (MFA) 吳沛勳劉冠成韓仁智

Recognition and Matching based on local invariant features Cordelia Schmid INRIA, Grenoble David Lowe Univ. of British Columbia.

General Tensor Discriminant Analysis and Gabor Features for Gait Recognition by D. Tao, X. Li, and J. Maybank, TPAMI 2007 Presented by Iulian Pruteanu.

1 Recognition by Appearance Appearance-based recognition is a competing paradigm to features and alignment. No features are extracted! Images are represented.

Local Non-Negative Matrix Factorization as a Visual Representation Tao Feng, Stan Z. Li, Heung-Yeung Shum, HongJiang Zhang 2002 IEEE Presenter : 張庭豪.

Texture scale and image segmentation using wavelet filters Stability of the features Through the study of stability of the eigenvectors and the eigenvalues.

Using Support Vector Machines to Enhance the Performance of Bayesian Face Recognition IEEE Transaction on Information Forensics and Security Zhifeng Li,

Classification Course web page: vision.cis.udel.edu/~cv May 12, 2003  Lecture 33.

Face Recognition: An Introduction

Histograms of Oriented Gradients for Human Detection(HOG)

Discriminant Analysis

A NOVEL METHOD FOR COLOR FACE RECOGNITION USING KNN CLASSIFIER

Feature extraction using fuzzy complete linear discriminant analysis The reporter ： Cui Yan

Linear Algebra Diyako Ghaderyan 1 Contents:  Linear Equations in Linear Algebra  Matrix Algebra  Determinants  Vector Spaces  Eigenvalues.

Supervisor: Nakhmani Arie Semester: Winter 2007 Target Recognition Harmatz Isca.

PCA vs ICA vs LDA. How to represent images? Why representation methods are needed?? –Curse of dimensionality – width x height x channels –Noise reduction.

A TUTORIAL ON SUPPORT VECTOR MACHINES FOR PATTERN RECOGNITION ASLI TAŞÇI Christopher J.C. Burges, Data Mining and Knowledge Discovery 2, , 1998.

March 2006Alon Slapak 1 of 32 Feature extraction A practical approach Feature selection Data synthesis Separability Feature extraction Guidelines Cluster.

Dimensionality reduction

Linear Algebra Diyako Ghaderyan 1 Contents:  Linear Equations in Linear Algebra  Matrix Algebra  Determinants  Vector Spaces  Eigenvalues.

Irfan Ullah Department of Information and Communication Engineering Myongji university, Yongin, South Korea Copyright © solarlits.com.

Principal Component Analysis and Linear Discriminant Analysis for Feature Reduction Jieping Ye Department of Computer Science and Engineering Arizona State.

Feature Extraction 主講人：虞台文. Content Principal Component Analysis (PCA) PCA Calculation — for Fewer-Sample Case Factor Analysis Fisher’s Linear Discriminant.

2D-LDA: A statistical linear discriminant analysis for image matrix

Face Recognition and Feature Subspaces Devi Parikh Virginia Tech 11/05/15 Slides borrowed from Derek Hoiem, who borrowed some slides from Lana Lazebnik,

3D Face Recognition Using Range Images Literature Survey Joonsoo Lee 3/10/05.

Feature Extraction 主講人：虞台文.

Chapter 13 Discrete Image Transforms

Multi-View Discriminant Analysis 多视判别分析

Septian Adi Wijaya – Informatics Brawijaya University

From: A new analytical method for characterizing nonlinear visual processes with stimuli of arbitrary distribution: Theory and applications Journal of.

Recognition: Face Recognition

Announcements Project 1 artifact winners

Linear Discriminant Analysis(LDA)

Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John.

Announcements Project 4 out today Project 2 winners help session today

Linear Algebra Lecture 32.

 = N  N matrix multiplication N = 3 matrix N = 3 matrix N = 3 matrix

Detecting Hidden Message Using Higher Order Statistical Models Hany Farid By Jingyu Ye Yiqi Hu.

Recognition and Matching based on local invariant features

The “Margaret Thatcher Illusion”, by Peter Thompson

Presentation transcript:

Zhiming Liu and Chengjun Liu, IEEE

Introduction Algorithms and procedures Experiments Conclusion

Uncontrolled illumination Color information and frequency features derived by means of the Discrete Fourier Transform (DFT) help improve face recognition performance. This correspondence presents a novel hybrid Color and Frequency Features (CFF) method for face recognition.

RIQ : R of the RGB + IQ of the YIQ

Initial image R, I, Q Real part Discrete Fourier transform of a face image Imaginary part MagnitudeMask

X :a random vector representing a frequency pattern vector. : are the eigenvectors  largest eigenvalues of X

FLD: S (w) : within-class scatter matrix S (b) : between-class scatter matrix,

Database: FRGC version 2 Experiment 4 the Training set contains images that are either controlled or uncontrolled.

Future research will consider applying kernel methods, such as the multiclass Kernel Fisher Analysis (KFA) method presented in [9], to replace the EFM method for improving face recognition performance. The hybrid color space improves face recognition performance.

Thanks for your attention !!!