Encyclopedia of Data Warehousing and Mining
|
|
- Earl Park
- 5 years ago
- Views:
Transcription
1 Encyclopedia of Data Warehousing and Mining Second Edition John Wang Montclair State University, USA Volume II Data Pro-I Information Science reference Hershey New York
2 Director of Editorial Content: Director of Production: Managing Editor: Assistant Managing Editor: Typesetter: Cover Design: Printed at: Kristin Klinger Jennifer Neidig Jamie Snavely Carole Coulson Amanda Appicello, Jeff Ash, Mike Brehem, Carole Coulson, Elizabeth Duke, Jen Henderson, Chris Hrobak, Jennifer Neidig, Jamie Snavely, Sean Woznicki Lisa Tosheff Yurchak Printing Inc. Published in the United States of America by Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue, Suite 200 Hershey PA Tel: Fax: Web site: and in the United Kingdom by Information Science Reference (an imprint of IGI Global) 3 Henrietta Street Covent Garden London WC2E 8LU Tel: Fax: Web site: Copyright 2009 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Encyclopedia of data warehousing and mining / John Wang, editor. -- 2nd ed. p. cm. Includes bibliographical references and index. Summary: "This set offers thorough examination of the issues of importance in the rapidly changing field of data warehousing and mining"--provided by publisher. ISBN (hardcover) -- ISBN (ebook) 1. Data mining. 2. Data warehousing. I. Wang, John, QA76.9.D37E dc British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this encyclopedia set is new, previously-unpublished material. The views expressed in this encyclopedia set are those of the authors, but not necessarily of the publisher. If a library purchased a print copy of this publication, please go to for information on activating the library's complimentary electronic access to this publication.
3 Section: Facial Recognition 857 Facial Recognition Rory A. Lewis UNC-Charlotte, USA F Zbigniew W. Ras University of North Carolina, Charlotte, USA INTRODUCTION Over the past decade Facial Recognition has become more cohesive and reliable than ever before. We begin with an analysis explaining why certain facial recognition methodologies examined under FERET, FRVT 2000, FRVT 2002, and FRVT 2006 have become stronger and why other approaches to facial recognition are losing traction. Second, we cluster the stronger approaches in terms of what approaches are mutually inclusive or exclusive to surrounding methodologies. Third, we discuss and compare emerging facial recognition technology in light of the aforementioned clusters. In conclusion, we suggest a road map that takes into consideration the final goals of each cluster, that given each clusters weakness, will make it easier to combine methodologies with surrounding clusters. BACKGROUND The National Institute of Standards and Technology (NIST) sponsored 2006 Face Recognition Vendor Test (FRVT) which is the most recent large scale independent synopsis of the state-of-the-art for face recognition systems. The previous tests in the series were the FERET, FRVT 2000, and FRVT The following organizations participated in the FRVT 2006 evaluation: Animetrics, Inc., Carnegie Mellon University, Cognitec Systems GmbH, Diamond Information Systems (DIS), Geometrix, Inc., Guardia, Identix, Inc., Neven Vision, New Jersey Institute of Technology (NJIT), Nivis, LLC, Old Dominion University, Panvista Limited, Peking University, Center for Information Science, PeopleSpot Inc., Rafael Armament Development Authority Ltd., SAGEM SA, Samsung Advanced Institute of Technology (SAIT), Tsinghua University, Tili Technology Limited, Toshiba Corporation, University of Houston, and Viisage. It should be noted that while the FRVT 2006 was conducted by the National Institute of Standards and Technology (NIST), it was jointly sponsored by five other U.S. Government agencies which share NIST s interest in measuring the improvements in face recognition technologies: Federal Bureau of Investigation, National Institute of Justice, National Institute of Standards and Technology, U.S. Department of Homeland Security, and the Transportation Security Administration. The FRVT 2006 measured the progress of facial recognition systems including commercial systems that used Windows or Linux based algorithms. The sequestered data comprised a large standard dataset of full frontal pictures provided to NIST by the U.S. State Department using non-conforming pixel resolutions and lighting angles of 36,000 pictures of persons applying for non-immigrant visas at U.S. consulates in Mexico. The tests evaluated 4 dimensions of facial recognition: high resolution still imagery, 3D facial scans, multi-sample still facial imagery, and pre-processing algorithms that compensate for pose and illumination. The results of the best of the 13 groups that entered have improved remarkably; the best algorithms in the FRVT 2002 computed 20% false rejections compared to only 1% false rejections in the FRVT 2006 tests. However, some of the groups that entered FRVT 2006 had results no better than that of In the tests, the rejection was less palatable: 12% for the best algorithms which still it is better than the 29% rejection rate of the 2002 tests. FRVT tests digress from the traditional facial recognition tests of the 1990 s in two ways: First, speed was not the issue in the tests, some of the algorithms took hundreds of hours to find matches in the database. The correct identification (precision) is the issue. Secondly, rather than the traditional ID searches of comparing a face in the camera with every face in the database for a match, the FRVT tests comprised security verification: is the face of the person standing in front of the Copyright 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
4 Facial Recognition Figure 1. The reduction in error rates of facial recognition algorithms camera claiming to be Mr. Whomever indeed the Mr. Whomever whose picture is in the database? THE STATE-OF-THE-ART OF THE 13 GROUPS IN FRVT 2006 The three well known facial recognition corporations, Google owned Neven Vision, Viisage Technology (owned by L-1 Identity Solutions), and Cognitec Systems of Germany performed the best. The two universities that excelled were the University of Houston and Tsinghua University in China. The four methodology clusters are (i) Support Vector Machines, (ii) Manifold/Eigenface, (iii) Principal Component Analysis with Modified Sum Square Error (PCA/SSE), and Pure Eigenface technology: 1. Cognitec Systems - Cognitec Systems FaceVACSincorporates Support Vector Machines (SVM) to capture facial features. In the event of a positive match, the authorized person is granted access to a PC (Thalheim, 2002). 2. Neven Vision - In 2002, Neven s Eyematic team achieved high scores in the Face Recognition Vendor Test (Neven, 2004). Neven Vision incorporates Eigenface Technology. The Neven methodology trains an RBF network which constructs a full manifold representation in a universal Eigenspace from a single view of an arbitrary pose. 3. L-1 Identity Solutions Inc L-1 s performance ranked it near or at the top of every NIST test. This validated the functionality of the algorithms that drive L-1 s facial and iris biometric solutions. L-1 was formed in 2006 when Viisage Technology Inc. bought Indentix Inc. The NIST evaluations of facial and iris technology covered algorithms submitted by Viisage and Indentix, both of which utilize variations of Eigenvalues, one called the principal component analysis (PCA)-based face recognition method and the other, the modified sum square error (SSE)-based distance technique. 4. M A. Turk and A P. Pentland they wrote a paper that changed the facial recognition world - Face 858
5 Facial Recognition Recognition Using Eigenfaces (Turk & Pentland, 1991) which won the Most Influential Paper of the Decade award from the IAPR MVA. In short, the new technology reconstructed a face by superimposing a set of Eigenfaces. The similarity between the two facial images is determined based on the coefficient of the relevant Eigenfaces. The images consist of a set of linear eigenvectors wherein the operators are non-zero vectors which result in a scalar multiple of themselves (Pentland, 1993). It is the scalar multiple which is referred to as the Eigenvalue associated with a particular eigenvector (Pentland & Kirby, 1987). Turk assumes that any human face is merely a combination of parts of the Eigenfaces. One person s face may consist of 25% from face 1, 25% from face 2, 4% of face 3, n% of face X (Kirby, 1990). Figure 2. Original picture set The authors propose that even though the technology is improving, as exemplified by the FRVT 2006 test results, the common dominator for problematic recognition is based on faces with erroneous and varying light source conditions. Under this assumption, 3-D models were compared with the related 2-D images in the data set. All four of the aforementioned primary methodologies of facial recognition across the board, performed poorly in this context: for the controlled illumination experiments, performance of the very-high resolution dataset was better than the high-resolution dataset. Also, from comparing the photos of faces in the database to the angle of view of the person in 3d/real life, an issue FRVT did not even consider, leads one to believe that light source variation on 3-d models compromises the knowledge discovery abilities of all the facial recognition algorithms at FRVT Figure 3. RGB normalization Figure 4. Grayscale and posturization 859 F
6 Facial Recognition INNERTRON TECHNOLOGY Major components of interactive visual data analysis and their functions that make knowledge extraction more effective are the current research theme in this field. Consider the 8 images of the subject photographed in Figure 2. These photos were taken at four different times over the period of 18 months under 4 varying sets of light sources. To the human brain this is clearly the same person without a doubt. However, after normalizing the light in RGB in each of the four sets, we yield the results displayed in Figure 3. Note that this normalization process is normalizing each group of four until the desired, empirical normalization matrix in RGB is achieved as in the manner that a machine would render the normalization process. Note that in Figure 2 the bottom right hand figure is not darkened at all but after normalization in Figure 3 it appears darker. In Figure 4, a simple Grayscale and 6-level posturization is performed. In Figure 5, as in the FRVT 2006 tests, shadow is added to compensate for distance measures. It is evident that varying light conditions change the dynamics of a face in a far more complex manner than facial surgery could. This is evident when one looks at the 2-d images in terms of what is white and what is black, measuring and storing where they are black and white. Innertron facial recognition technology proposes a solution to the core, common denominator problem of the FRVT 2006 tests as illustrated in Figures 2 through 5, where classical lighting and shadow normalization can corrupt lighting on some good images in order to compensate for bad lighting at the level of the median lighting parameters. The Lambertian model states that the image of a 3D object is subject to three factors, namely the surface normal, albedo, and lighting. These can be factorized into an intrinsic part of the surface normal and albedo, and an extrinsic part of lighting (Wang 2004). Figure 5. Distance configuration Figure 6. Difference Eigen 860
7 Facial Recognition where p is the surface texture (albedo) of the face, n ( x, y ) T represents the 3-D rendering of the normal surface of the face, which will be the same for all objects of the class, and s is the position of the light source, which may vary arbitrarily. The quotient image Q y of face y against face a is defined by where u and v range over the image, I y is an image of object y with the illumination direction s y, and x j are combining coefficients estimated by Least Square based on training set (Shashua, 2001), (Raviv, 1999), and I 1, I 2, I 3 are three non-collinearly illuminated images. This is shown in Figure 6 where we superimpose, using negative alpha values, face y onto face a with the higher level image additionally set to a basic Eigenvector exposing the differences of itself a and the image below it y. This cancels out erroneous shadows, and only keeps constant data. The color of the images has changed from that of the original images in Figure 2. This is because of the effect the Eigenvector has on two samples from almost the same place and time. One can also see in Figure 7 that the large amount of shadows that were present in Figures 3 through 4 are almost gone. In Figure 7 and Figure 8 the innertron based strategy has normalized the face. See details in (Lewis, Ras 2005). In our approach, we use training database of human faces described by a set of innertron features to built classifiers for face recognition. FUTURE TRENDS F Figure 7. Calculate tilt Figure 8. Normalize - grid There are many projects focused on creation of intelligent methods for solving many difficult high-level image feature extraction and data analysis problems in which both local and global properties as well as spatial relations are taken into consideration. Some of these new techniques work in pixel and compressed domain (avoiding the inverse transform in decompression), thus speeding up the whole process (Delac & Grgic, 2007), (Deniz et al., 2001), (Du et al., 2005). The development of these new high-level image features is essential for the successful construction of new classifiers for the face recognition. Knowledge discovery techniques are especially promising in providing tools to build classifiers for recognizing emotions associated with human faces (happy, sad, surprised,..). But this area is still at the very beginning stage. CONCLUSION Overcoming the challenges of shadow elimination seems promising with the use of negative alpha values set to basic Eigenvectors particularly when used with the Lambertian methodology. Mining these images in a 2-D database is easier because the many levels of grey will be normalized to a specific set allowing a 861
8 Facial Recognition more precise vector value for distance functions. Pure Eigenvector technology, also fails when huge amounts of negatives are superimposed onto one another, once again pointing to normalization as the key. Data in the innertron database need to be consistent in tilt and size of the face. This means that before a face is submitted to the database, it also needs tilt and facial size normalization. REFERENCES Delac, K., Grgic, M. (2007). Face Recognition, I-Tech Education and Publishing, Vienna, 558 pages. Deniz, O., Castrillon, M., Hernández, M. (2001). Face recognition using independent component analysis and support vector machines, Proceedings of Audio- and Video-Based Biometric Person Authentication: Third International Conference, LNCS 2091, Springer. Du, W., Inoue, K., Urahama, K. (2005). Dimensionality reduction for semi-supervised face recognition, Fuzzy Systems and Knowledge Discovery (FSKD), LNAI , Springer, Kirby, M., Sirovich, L. (1990). Application of the Karhunen-Loeve procedure for the characterization of human faces, IEEE Transactions on Pattern Analysis and Machine Intelligence 12(1), Lewis, R.A., Ras, Z.W. (2005). New methodology of facial recognition, Intelligent Information Processing and Web Mining, Advances in Soft Computing, Proceedings of the IIS 2005 Symposium, Gdansk, Poland, Springer, Neven, H. (2004). Neven Vision Machine Technology, See: neven.html Pentland, L., Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces, Journal of the Optical Society of America 4, Pentland, A., Moghaddam, B., Starner, T., Oliyide, O., Turk, M. (1993). View-Based and modular eigenspaces for face recognition, Technical Report 245, MIT Media Lab. Riklin-Raviv, T., Shashua, A. (1999). The Quotient image: class based recognition and synthesis under varying illumination, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Shashua, A., Riklin-Raviv, T. (2001). The quotient image: class-based re-rendering and recognition with varying illuminations, Transactions on Pattern Analysis and Machine Intelligence 23(2), Turk M., Pentland, A. (1991). Face recognition using eigenfaces, Proc. IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Conference, Wang, H., Li, S., Wang, Y. (2004). Face recognition under varying lighting conditions using self quotient image, Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, KEY TERMS Classifier: Compact description of a set of instances that have common behavioral and structural features (attributes and operations, respectively). Eigenfaces: Set of eigenvectors used in the computer vision problem of human face recognition. Eigenvalue: Scalar associated with a given linear transformation of a vector space and having the property that there is some nonzero vector which when multiplied by the scalar is equal to the vector obtained by letting the transformation operate on the vector. Facial Recognition: Process which automatically identifies a person from a digital image or a video frame from a video source. Knowledge Discovery: Process of automatically searching large volumes of data for patterns that can be considered as knowledge about the data. Support Vector Machines (SVM): Set of related supervised learning methods used for classification and regression. They belong to a family of generalized linear classifiers. A special property of SVMs is that they simultaneously minimize the empirical classification error and maximize the geometric margin; hence they are also known as maximum margin classifiers. 862
Encyclopedia of Information Science and Technology
Encyclopedia of Information Science and Technology Second Edition Mehdi Khosrow-Pour Information Resources Management Association, USA Volume IV G-Internet INFORMATION SCIENCE REFERENCE Hershey New York
More informationEncyclopedia of Information Science and Technology
Encyclopedia of Information Science and Technology Second Edition Mehdi Khosrow-Pour Information Resources Management Association, USA Volume IV G-Internet INFORMATION SCIENCE REFERENCE Hershey New York
More informationEncyclopedia of Data Warehousing and Mining
Encyclopedia of Data Warehousing and Mining Second Edition John Wang Montclair State University, USA Volume III K-Pri Information Science reference Hershey New York Director of Editorial Content: Director
More informationNIST. Support Vector Machines. Applied to Face Recognition U56 QC 100 NO A OS S. P. Jonathon Phillips. Gaithersburg, MD 20899
^ A 1 1 1 OS 5 1. 4 0 S Support Vector Machines Applied to Face Recognition P. Jonathon Phillips U.S. DEPARTMENT OF COMMERCE Technology Administration National Institute of Standards and Technology Information
More informationLinear Discriminant Analysis for 3D Face Recognition System
Linear Discriminant Analysis for 3D Face Recognition System 3.1 Introduction Face recognition and verification have been at the top of the research agenda of the computer vision community in recent times.
More informationPrincipal Component Analysis and Neural Network Based Face Recognition
Principal Component Analysis and Neural Network Based Face Recognition Qing Jiang Mailbox Abstract People in computer vision and pattern recognition have been working on automatic recognition of human
More informationOn Modeling Variations for Face Authentication
On Modeling Variations for Face Authentication Xiaoming Liu Tsuhan Chen B.V.K. Vijaya Kumar Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213 xiaoming@andrew.cmu.edu
More informationFace Recognition for Different Facial Expressions Using Principal Component analysis
Face Recognition for Different Facial Expressions Using Principal Component analysis ASHISH SHRIVASTAVA *, SHEETESH SAD # # Department of Electronics & Communications, CIIT, Indore Dewas Bypass Road, Arandiya
More informationEncyclopedia of Information Science and Technology
Encyclopedia of Information Science and Technology Second Edition Mehdi Khosrow-Pour Information Resources Management Association, USA Volume VI Mu-Q Information Science reference Hershey New York Director
More informationFace detection and recognition. Many slides adapted from K. Grauman and D. Lowe
Face detection and recognition Many slides adapted from K. Grauman and D. Lowe Face detection and recognition Detection Recognition Sally History Early face recognition systems: based on features and distances
More informationImage Processing and Image Representations for Face Recognition
Image Processing and Image Representations for Face Recognition 1 Introduction Face recognition is an active area of research in image processing and pattern recognition. Since the general topic of face
More informationIntelligence Integration in Distributed Knowledge Management
Intelligence Integration in Distributed Knowledge Management Dariusz Król Wroclaw University of Technology, Poland Ngoc Thanh Nguyen Wroclaw University of Technology, Poland InformatIon science reference
More informationSemi-Supervised PCA-based Face Recognition Using Self-Training
Semi-Supervised PCA-based Face Recognition Using Self-Training Fabio Roli and Gian Luca Marcialis Dept. of Electrical and Electronic Engineering, University of Cagliari Piazza d Armi, 09123 Cagliari, Italy
More informationComparison of Different Face Recognition Algorithms
Comparison of Different Face Recognition Algorithms Pavan Pratap Chauhan 1, Vishal Kumar Lath 2 and Mr. Praveen Rai 3 1,2,3 Computer Science and Engineering, IIMT College of Engineering(Greater Noida),
More informationFace Recognition using Eigenfaces SMAI Course Project
Face Recognition using Eigenfaces SMAI Course Project Satarupa Guha IIIT Hyderabad 201307566 satarupa.guha@research.iiit.ac.in Ayushi Dalmia IIIT Hyderabad 201307565 ayushi.dalmia@research.iiit.ac.in Abstract
More informationFACE RECOGNITION USING SUPPORT VECTOR MACHINES
FACE RECOGNITION USING SUPPORT VECTOR MACHINES Ashwin Swaminathan ashwins@umd.edu ENEE633: Statistical and Neural Pattern Recognition Instructor : Prof. Rama Chellappa Project 2, Part (b) 1. INTRODUCTION
More informationEncyclopedia of Data Warehousing and Mining
Encyclopedia of Data Warehousing and Mining Second Edition John Wang Montclair State University, USA Volume II Data Pro-I Information Science reference Hershey New York Director of Editorial Content: Director
More informationLinear Discriminant Analysis in Ottoman Alphabet Character Recognition
Linear Discriminant Analysis in Ottoman Alphabet Character Recognition ZEYNEB KURT, H. IREM TURKMEN, M. ELIF KARSLIGIL Department of Computer Engineering, Yildiz Technical University, 34349 Besiktas /
More informationComputer Aided Drafting, Design and Manufacturing Volume 26, Number 2, June 2016, Page 8. Face recognition attendance system based on PCA approach
Computer Aided Drafting, Design and Manufacturing Volume 6, Number, June 016, Page 8 CADDM Face recognition attendance system based on PCA approach Li Yanling 1,, Chen Yisong, Wang Guoping 1. Department
More informationMobile Face Recognization
Mobile Face Recognization CS4670 Final Project Cooper Bills and Jason Yosinski {csb88,jy495}@cornell.edu December 12, 2010 Abstract We created a mobile based system for detecting faces within a picture
More informationFace Recognition using Principle Component Analysis, Eigenface and Neural Network
Face Recognition using Principle Component Analysis, Eigenface and Neural Network Mayank Agarwal Student Member IEEE Noida,India mayank.agarwal@ieee.org Nikunj Jain Student Noida,India nikunj262@gmail.com
More informationFace Recognition Using SIFT- PCA Feature Extraction and SVM Classifier
IOSR Journal of VLSI and Signal Processing (IOSR-JVSP) Volume 5, Issue 2, Ver. II (Mar. - Apr. 2015), PP 31-35 e-issn: 2319 4200, p-issn No. : 2319 4197 www.iosrjournals.org Face Recognition Using SIFT-
More informationIllumination invariant face recognition and impostor rejection using different MINACE filter algorithms
Illumination invariant face recognition and impostor rejection using different MINACE filter algorithms Rohit Patnaik and David Casasent Dept. of Electrical and Computer Engineering, Carnegie Mellon University,
More informationSubject-Oriented Image Classification based on Face Detection and Recognition
000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050
More informationComponent-based Face Recognition with 3D Morphable Models
Component-based Face Recognition with 3D Morphable Models B. Weyrauch J. Huang benjamin.weyrauch@vitronic.com jenniferhuang@alum.mit.edu Center for Biological and Center for Biological and Computational
More informationLOCAL APPEARANCE BASED FACE RECOGNITION USING DISCRETE COSINE TRANSFORM
LOCAL APPEARANCE BASED FACE RECOGNITION USING DISCRETE COSINE TRANSFORM Hazim Kemal Ekenel, Rainer Stiefelhagen Interactive Systems Labs, University of Karlsruhe Am Fasanengarten 5, 76131, Karlsruhe, Germany
More informationInternational Journal of Digital Application & Contemporary research Website: (Volume 1, Issue 8, March 2013)
Face Recognition using ICA for Biometric Security System Meenakshi A.D. Abstract An amount of current face recognition procedures use face representations originate by unsupervised statistical approaches.
More informationDimension Reduction CS534
Dimension Reduction CS534 Why dimension reduction? High dimensionality large number of features E.g., documents represented by thousands of words, millions of bigrams Images represented by thousands of
More informationFace detection and recognition. Detection Recognition Sally
Face detection and recognition Detection Recognition Sally Face detection & recognition Viola & Jones detector Available in open CV Face recognition Eigenfaces for face recognition Metric learning identification
More informationMulti-Modal Human Verification Using Face and Speech
22 Multi-Modal Human Verification Using Face and Speech Changhan Park 1 and Joonki Paik 2 1 Advanced Technology R&D Center, Samsung Thales Co., Ltd., 2 Graduate School of Advanced Imaging Science, Multimedia,
More informationDr. K. Nagabhushan Raju Professor, Dept. of Instrumentation Sri Krishnadevaraya University, Anantapuramu, Andhra Pradesh, India
Volume 6, Issue 10, October 2016 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Design and
More informationRecognition: Face Recognition. Linda Shapiro EE/CSE 576
Recognition: Face Recognition Linda Shapiro EE/CSE 576 1 Face recognition: once you ve detected and cropped a face, try to recognize it Detection Recognition Sally 2 Face recognition: overview Typical
More informationFace Recognition for Mobile Devices
Face Recognition for Mobile Devices Aditya Pabbaraju (adisrinu@umich.edu), Srujankumar Puchakayala (psrujan@umich.edu) INTRODUCTION Face recognition is an application used for identifying a person from
More informationThe Quotient Image: Class Based Recognition and Synthesis Under Varying Illumination Conditions
The Quotient Image: Class Based Recognition and Synthesis Under Varying Illumination Conditions Tammy Riklin-Raviv and Amnon Shashua Institute of Computer Science, The Hebrew University, Jerusalem 91904,
More informationFace Recognition using Laplacianfaces
Journal homepage: www.mjret.in ISSN:2348-6953 Kunal kawale Face Recognition using Laplacianfaces Chinmay Gadgil Mohanish Khunte Ajinkya Bhuruk Prof. Ranjana M.Kedar Abstract Security of a system is an
More informationFACE RECOGNITION BASED ON GENDER USING A MODIFIED METHOD OF 2D-LINEAR DISCRIMINANT ANALYSIS
FACE RECOGNITION BASED ON GENDER USING A MODIFIED METHOD OF 2D-LINEAR DISCRIMINANT ANALYSIS 1 Fitri Damayanti, 2 Wahyudi Setiawan, 3 Sri Herawati, 4 Aeri Rachmad 1,2,3,4 Faculty of Engineering, University
More informationDetermination of 3-D Image Viewpoint Using Modified Nearest Feature Line Method in Its Eigenspace Domain
Determination of 3-D Image Viewpoint Using Modified Nearest Feature Line Method in Its Eigenspace Domain LINA +, BENYAMIN KUSUMOPUTRO ++ + Faculty of Information Technology Tarumanagara University Jl.
More informationAn Efficient Secure Multimodal Biometric Fusion Using Palmprint and Face Image
International Journal of Computer Science Issues, Vol. 2, 2009 ISSN (Online): 694-0784 ISSN (Print): 694-084 49 An Efficient Secure Multimodal Biometric Fusion Using Palmprint and Face Image Nageshkumar.M,
More informationPrincipal Component Analysis (PCA) is a most practicable. statistical technique. Its application plays a major role in many
CHAPTER 3 PRINCIPAL COMPONENT ANALYSIS ON EIGENFACES 2D AND 3D MODEL 3.1 INTRODUCTION Principal Component Analysis (PCA) is a most practicable statistical technique. Its application plays a major role
More informationSpatial Frequency Domain Methods for Face and Iris Recognition
Spatial Frequency Domain Methods for Face and Iris Recognition Dept. of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh, PA 15213 e-mail: Kumar@ece.cmu.edu Tel.: (412) 268-3026
More informationA GENERIC FACE REPRESENTATION APPROACH FOR LOCAL APPEARANCE BASED FACE VERIFICATION
A GENERIC FACE REPRESENTATION APPROACH FOR LOCAL APPEARANCE BASED FACE VERIFICATION Hazim Kemal Ekenel, Rainer Stiefelhagen Interactive Systems Labs, Universität Karlsruhe (TH) 76131 Karlsruhe, Germany
More informationRecognition of Non-symmetric Faces Using Principal Component Analysis
Recognition of Non-symmetric Faces Using Principal Component Analysis N. Krishnan Centre for Information Technology & Engineering Manonmaniam Sundaranar University, Tirunelveli-627012, India Krishnan17563@yahoo.com
More informationMulti-Pose Face Recognition And Tracking System
Available online at www.sciencedirect.com Procedia Computer Science 6 (2011) 381 386 Complex Adaptive Systems, Volume 1 Cihan H. Dagli, Editor in Chief Conference Organized by Missouri University of Science
More informationUnsupervised learning in Vision
Chapter 7 Unsupervised learning in Vision The fields of Computer Vision and Machine Learning complement each other in a very natural way: the aim of the former is to extract useful information from visual
More informationApplications Video Surveillance (On-line or off-line)
Face Face Recognition: Dimensionality Reduction Biometrics CSE 190-a Lecture 12 CSE190a Fall 06 CSE190a Fall 06 Face Recognition Face is the most common biometric used by humans Applications range from
More informationEncyclopedia of Data Warehousing and Mining
Encyclopedia of Data Warehousing and Mining John Wang Montclair State University, USA Volume I A-H IDEA GROUP REFERENCE Hershey London Melbourne Singapore Acquisitions Editor: Development Editor: Senior
More informationFace Recognition using Several Levels of Features Fusion
Face Recognition using Several Levels of Features Fusion Elizabeth García-Rios, Gualberto Aguilar-Torres, Enrique Escamilla-Hernandez, Omar Jacobo-Sanchez 2, ariko Nakano-iyatake, Hector Perez-eana echanical
More informationFace recognition based on improved BP neural network
Face recognition based on improved BP neural network Gaili Yue, Lei Lu a, College of Electrical and Control Engineering, Xi an University of Science and Technology, Xi an 710043, China Abstract. In order
More informationBiometrics Technology: Image Processing & Pattern Recognition (by Dr. Dickson Tong)
Biometrics Technology: Image Processing & Pattern Recognition (by Dr. Dickson Tong) References: [1] http://homepages.inf.ed.ac.uk/rbf/hipr2/index.htm [2] http://www.cs.wisc.edu/~dyer/cs540/notes/vision.html
More informationWe are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists. International authors and editors
We are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists 4,000 6,000 M Open access books available International authors and editors Downloads Our authors
More informationPerformance Evaluation of Optimised PCA and Projection Combined PCA methods in Facial Images
Journal of Computations & Modelling, vol.2, no.3, 2012, 17-29 ISSN: 1792-7625 (print), 1792-8850 (online) Scienpress Ltd, 2012 Performance Evaluation of Optimised PCA and Projection Combined PCA methods
More informationCHAPTER 3 PRINCIPAL COMPONENT ANALYSIS AND FISHER LINEAR DISCRIMINANT ANALYSIS
38 CHAPTER 3 PRINCIPAL COMPONENT ANALYSIS AND FISHER LINEAR DISCRIMINANT ANALYSIS 3.1 PRINCIPAL COMPONENT ANALYSIS (PCA) 3.1.1 Introduction In the previous chapter, a brief literature review on conventional
More informationFacial Expression Recognition Using Non-negative Matrix Factorization
Facial Expression Recognition Using Non-negative Matrix Factorization Symeon Nikitidis, Anastasios Tefas and Ioannis Pitas Artificial Intelligence & Information Analysis Lab Department of Informatics Aristotle,
More informationNOWADAYS, there are many human jobs that can. Face Recognition Performance in Facing Pose Variation
CommIT (Communication & Information Technology) Journal 11(1), 1 7, 2017 Face Recognition Performance in Facing Pose Variation Alexander A. S. Gunawan 1 and Reza A. Prasetyo 2 1,2 School of Computer Science,
More informationAnnouncements. Recognition I. Gradient Space (p,q) What is the reflectance map?
Announcements I HW 3 due 12 noon, tomorrow. HW 4 to be posted soon recognition Lecture plan recognition for next two lectures, then video and motion. Introduction to Computer Vision CSE 152 Lecture 17
More informationEncyclopedia of Multimedia Technology and Networking
Encyclopedia of Multimedia Technology and Networking Second Edition Margherita Pagani Bocconi University, Italy Volume III O-Z Information Science reference Hershey New York Director of Editorial Content:
More informationClass-based Multiple Light Detection: An Application to Faces
Class-based Multiple Light Detection: An Application to Faces Christos-Savvas Bouganis and Mike Brookes Department of Electrical and Electronic Engineering Imperial College of Science, Technology and Medicine
More informationIllumination-Robust Face Recognition based on Gabor Feature Face Intrinsic Identity PCA Model
Illumination-Robust Face Recognition based on Gabor Feature Face Intrinsic Identity PCA Model TAE IN SEOL*, SUN-TAE CHUNG*, SUNHO KI**, SEONGWON CHO**, YUN-KWANG HONG*** *School of Electronic Engineering
More informationA STUDY FOR THE SELF SIMILARITY SMILE DETECTION
A STUDY FOR THE SELF SIMILARITY SMILE DETECTION D. Freire, L. Antón, M. Castrillón. SIANI, Universidad de Las Palmas de Gran Canaria, Spain dfreire@iusiani.ulpgc.es, lanton@iusiani.ulpgc.es,mcastrillon@iusiani.ulpgc.es
More informationWebpage: Volume 3, Issue VII, July 2015 ISSN
Independent Component Analysis (ICA) Based Face Recognition System S.Narmatha 1, K.Mahesh 2 1 Research Scholar, 2 Associate Professor 1,2 Department of Computer Science and Engineering, Alagappa University,
More informationAn Integration of Face detection and Tracking for Video As Well As Images
An Integration of Face detection and Tracking for Video As Well As Images Manish Trivedi 1 Ruchi Chaurasia 2 Abstract- The objective of this paper is to evaluate various face detection and recognition
More informationRecognition, SVD, and PCA
Recognition, SVD, and PCA Recognition Suppose you want to find a face in an image One possibility: look for something that looks sort of like a face (oval, dark band near top, dark band near bottom) Another
More informationFace recognition algorithms: performance evaluation
Face recognition algorithms: performance evaluation Project Report Marco Del Coco - Pierluigi Carcagnì Institute of Applied Sciences and Intelligent systems c/o Dhitech scarl Campus Universitario via Monteroni
More informationA Matlab based Face Recognition GUI system Using Principal Component Analysis and Artificial Neural Network
A Matlab based Face Recognition GUI system Using Principal Component Analysis and Artificial Neural Network Achala Khandelwal 1 and Jaya Sharma 2 1,2 Asst Prof Department of Electrical Engineering, Shri
More informationAN EXAMINING FACE RECOGNITION BY LOCAL DIRECTIONAL NUMBER PATTERN (Image Processing)
AN EXAMINING FACE RECOGNITION BY LOCAL DIRECTIONAL NUMBER PATTERN (Image Processing) J.Nithya 1, P.Sathyasutha2 1,2 Assistant Professor,Gnanamani College of Engineering, Namakkal, Tamil Nadu, India ABSTRACT
More informationColor Space Projection, Feature Fusion and Concurrent Neural Modules for Biometric Image Recognition
Proceedings of the 5th WSEAS Int. Conf. on COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS AND CYBERNETICS, Venice, Italy, November 20-22, 2006 286 Color Space Projection, Fusion and Concurrent Neural
More informationImage Based Feature Extraction Technique For Multiple Face Detection and Recognition in Color Images
Image Based Feature Extraction Technique For Multiple Face Detection and Recognition in Color Images 1 Anusha Nandigam, 2 A.N. Lakshmipathi 1 Dept. of CSE, Sir C R Reddy College of Engineering, Eluru,
More informationFacial Expression Detection Using Implemented (PCA) Algorithm
Facial Expression Detection Using Implemented (PCA) Algorithm Dileep Gautam (M.Tech Cse) Iftm University Moradabad Up India Abstract: Facial expression plays very important role in the communication with
More informationFace Recognition System Using PCA
Face Recognition System Using PCA M.V.N.R. Pavan Kumar 1, Shaikh Arshad A. 2, Katwate Dhananjay P. 3,Jamdar Rohit N. 4 Department of Electronics and Telecommunication Engineering 1,2,3,4, LNBCIET, Satara-415020
More informationA Study on Similarity Computations in Template Matching Technique for Identity Verification
A Study on Similarity Computations in Template Matching Technique for Identity Verification Lam, S. K., Yeong, C. Y., Yew, C. T., Chai, W. S., Suandi, S. A. Intelligent Biometric Group, School of Electrical
More informationDISTANCE MAPS: A ROBUST ILLUMINATION PREPROCESSING FOR ACTIVE APPEARANCE MODELS
DISTANCE MAPS: A ROBUST ILLUMINATION PREPROCESSING FOR ACTIVE APPEARANCE MODELS Sylvain Le Gallou*, Gaspard Breton*, Christophe Garcia*, Renaud Séguier** * France Telecom R&D - TECH/IRIS 4 rue du clos
More informationGENDER CLASSIFICATION USING SUPPORT VECTOR MACHINES
GENDER CLASSIFICATION USING SUPPORT VECTOR MACHINES Ashwin Swaminathan ashwins@umd.edu ENEE633: Statistical and Neural Pattern Recognition Instructor : Prof. Rama Chellappa Project 2, Part (a) 1. INTRODUCTION
More informationCOMBINED METHOD TO VISUALISE AND REDUCE DIMENSIONALITY OF THE FINANCIAL DATA SETS
COMBINED METHOD TO VISUALISE AND REDUCE DIMENSIONALITY OF THE FINANCIAL DATA SETS Toomas Kirt Supervisor: Leo Võhandu Tallinn Technical University Toomas.Kirt@mail.ee Abstract: Key words: For the visualisation
More informationGender Classification Technique Based on Facial Features using Neural Network
Gender Classification Technique Based on Facial Features using Neural Network Anushri Jaswante Dr. Asif Ullah Khan Dr. Bhupesh Gour Computer Science & Engineering, Rajiv Gandhi Proudyogiki Vishwavidyalaya,
More informationPCA and KPCA algorithms for Face Recognition A Survey
PCA and KPCA algorithms for Face Recognition A Survey Surabhi M. Dhokai 1, Vaishali B.Vala 2,Vatsal H. Shah 3 1 Department of Information Technology, BVM Engineering College, surabhidhokai@gmail.com 2
More informationHaresh D. Chande #, Zankhana H. Shah *
Illumination Invariant Face Recognition System Haresh D. Chande #, Zankhana H. Shah * # Computer Engineering Department, Birla Vishvakarma Mahavidyalaya, Gujarat Technological University, India * Information
More informationARE you? CS229 Final Project. Jim Hefner & Roddy Lindsay
ARE you? CS229 Final Project Jim Hefner & Roddy Lindsay 1. Introduction We use machine learning algorithms to predict attractiveness ratings for photos. There is a wealth of psychological evidence indicating
More informationData Mining Final Project Francisco R. Ortega Professor: Dr. Tao Li
Data Mining Final Project Francisco R. Ortega Professor: Dr. Tao Li FALL 2009 1.Introduction In the data mining class one of the aspects of interest were classifications. For the final project, the decision
More informationDigital Vision Face recognition
Ulrik Söderström ulrik.soderstrom@tfe.umu.se 27 May 2007 Digital Vision Face recognition 1 Faces Faces are integral to human interaction Manual facial recognition is already used in everyday authentication
More informationA Hierarchical Face Identification System Based on Facial Components
A Hierarchical Face Identification System Based on Facial Components Mehrtash T. Harandi, Majid Nili Ahmadabadi, and Babak N. Araabi Control and Intelligent Processing Center of Excellence Department of
More informationTraining Algorithms for Robust Face Recognition using a Template-matching Approach
Training Algorithms for Robust Face Recognition using a Template-matching Approach Xiaoyan Mu, Mehmet Artiklar, Metin Artiklar, and Mohamad H. Hassoun Department of Electrical and Computer Engineering
More informationHybrid Face Recognition and Classification System for Real Time Environment
Hybrid Face Recognition and Classification System for Real Time Environment Dr.Matheel E. Abdulmunem Department of Computer Science University of Technology, Baghdad, Iraq. Fatima B. Ibrahim Department
More informationHeat Kernel Based Local Binary Pattern for Face Representation
JOURNAL OF LATEX CLASS FILES 1 Heat Kernel Based Local Binary Pattern for Face Representation Xi Li, Weiming Hu, Zhongfei Zhang, Hanzi Wang Abstract Face classification has recently become a very hot research
More informationTwo-View Face Recognition Using Bayesian Fusion
Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA - October 2009 Two-View Face Recognition Using Bayesian Fusion Grace Shin-Yee Tsai Department
More information[Gaikwad *, 5(11): November 2018] ISSN DOI /zenodo Impact Factor
GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES LBP AND PCA BASED ON FACE RECOGNITION SYSTEM Ashok T. Gaikwad Institute of Management Studies and Information Technology, Aurangabad, (M.S), India ABSTRACT
More informationFace Recognition Using Vector Quantization Histogram and Support Vector Machine Classifier Rong-sheng LI, Fei-fei LEE *, Yan YAN and Qiu CHEN
2016 International Conference on Artificial Intelligence: Techniques and Applications (AITA 2016) ISBN: 978-1-60595-389-2 Face Recognition Using Vector Quantization Histogram and Support Vector Machine
More informationModel-Based Face Computation
Model-Based Face Computation 1. Research Team Project Leader: Post Doc(s): Graduate Students: Prof. Ulrich Neumann, IMSC and Computer Science John P. Lewis Hea-juen Hwang, Zhenyao Mo, Gordon Thomas 2.
More informationAutomated Canvas Analysis for Painting Conservation. By Brendan Tobin
Automated Canvas Analysis for Painting Conservation By Brendan Tobin 1. Motivation Distinctive variations in the spacings between threads in a painting's canvas can be used to show that two sections of
More informationObject. Radiance. Viewpoint v
Fisher Light-Fields for Face Recognition Across Pose and Illumination Ralph Gross, Iain Matthews, and Simon Baker The Robotics Institute, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213
More informationPose Normalization for Robust Face Recognition Based on Statistical Affine Transformation
Pose Normalization for Robust Face Recognition Based on Statistical Affine Transformation Xiujuan Chai 1, 2, Shiguang Shan 2, Wen Gao 1, 2 1 Vilab, Computer College, Harbin Institute of Technology, Harbin,
More informationFACE RECOGNITION FROM A SINGLE SAMPLE USING RLOG FILTER AND MANIFOLD ANALYSIS
FACE RECOGNITION FROM A SINGLE SAMPLE USING RLOG FILTER AND MANIFOLD ANALYSIS Jaya Susan Edith. S 1 and A.Usha Ruby 2 1 Department of Computer Science and Engineering,CSI College of Engineering, 2 Research
More informationCOMPUTER VISION FOR VISUAL EFFECTS
COMPUTER VISION FOR VISUAL EFFECTS Modern blockbuster movies seamlessly introduce impossible characters and action into real-world settings using digital visual effects. These effects are made possible
More informationComputationally Efficient Serial Combination of Rotation-invariant and Rotation Compensating Iris Recognition Algorithms
Computationally Efficient Serial Combination of Rotation-invariant and Rotation Compensating Iris Recognition Algorithms Andreas Uhl Department of Computer Sciences University of Salzburg, Austria uhl@cosy.sbg.ac.at
More informationModel-based Enhancement of Lighting Conditions in Image Sequences
Model-based Enhancement of Lighting Conditions in Image Sequences Peter Eisert and Bernd Girod Information Systems Laboratory Stanford University {eisert,bgirod}@stanford.edu http://www.stanford.edu/ eisert
More informationFace Detection and Recognition in an Image Sequence using Eigenedginess
Face Detection and Recognition in an Image Sequence using Eigenedginess B S Venkatesh, S Palanivel and B Yegnanarayana Department of Computer Science and Engineering. Indian Institute of Technology, Madras
More informationImage-Based Face Recognition using Global Features
Image-Based Face Recognition using Global Features Xiaoyin xu Research Centre for Integrated Microsystems Electrical and Computer Engineering University of Windsor Supervisors: Dr. Ahmadi May 13, 2005
More informationEnhanced Facial Expression Recognition using 2DPCA Principal component Analysis and Gabor Wavelets.
Enhanced Facial Expression Recognition using 2DPCA Principal component Analysis and Gabor Wavelets. Zermi.Narima(1), Saaidia.Mohammed(2), (1)Laboratory of Automatic and Signals Annaba (LASA), Department
More informationFace Recognition in Very Low Bit Rate SPIHT Compressed Facial Images
Face Recognition in Very Low Bit Rate SPIHT Compressed Facial Images Karuppana Gounder Somasundaram 1 and Nagappan Palaniappan 2 1 Image Processing Lab, Department of Computer Science and Applications,
More informationAn Efficient Face Recognition under Varying Image Conditions
An Efficient Face Recognition under Varying Image Conditions C.Kanimozhi,V.Nirmala Abstract Performance of the face verification system depends on many conditions. One of the most problematic is varying
More informationWaleed Pervaiz CSE 352
Waleed Pervaiz CSE 352 Computer Vision is the technology that enables machines to see and obtain information from digital images. It is seen as an integral part of AI in fields such as pattern recognition
More information