Challenges in Face Recognition Biometrics. Sujeewa Alwis Cybula Ltd

Size: px
Start display at page:

Download "Challenges in Face Recognition Biometrics. Sujeewa Alwis Cybula Ltd"

Transcription

1 Challenges in Face Recognition Biometrics Sujeewa Alwis Cybula Ltd

2 Background Techniques and issues Demo Questions

3 Why use face? Every one has got a fairly unique face Can be captured without user cooperation (passive)

4 Application Modes Verification Are you the same person you say you are? System captures a new biometric sample and the person submits an ID. Yes/no answer indicates authentication result. Identification Who are you? System captures a new biometric sample. It does a database search and presents the top n similar matches may need a human operator to make the final decision. Watch-list Are we looking for you System captures a new biometric sample. System triggers an alarm only if that person is in the database. Similar to identification - but uses an additional threshold to identify a hit.

5 Iris Advantages highly unique (five different patterns in even two identical twins) Stable after the first year of birth Disadvantages Need user cooperation Difficulties during enrolment The most successful technique is based on projecting Iris pattern onto a Gabor wavelet (Daugman, 1993). Gabor coefficients represent the biometric template - commercialised by Iridian technologies

6 Fingerprints Advantages Availability of large fingerprint databases Disadvantages Associated with crime control/investigation Need user cooperation Need to keep the capture surface clean and germ-free not suitable for high-throughput applications Represents minutiae points in a map Cross match technologies is one of the companies that sell fingerprint recognition systems

7 Gait recognition Palm print recognition Voice recognition

8 Combinations Face + Iris (Wang, 2003) Face + Ear (Chang, 2003) Face + Gait (Shakhnanorvich, 2002) Face + Palm print + Fingerprint (Ross, 2001) Face + Voice + Lip movement (Frischholz, 2000) Face + Voice (Kittler, 1997)

9 Face Representation 2D vs. 3D 2D Advantages Availability of large 2D image collections Capture devices are currently cheaper 3D Advantages Can deal with pose variations if the cameras can capture the full face Less sensitive to lighting variations Better accuracy during recognition (Experimental results from Notre Dame University, Chang et al. 2003)

10 Face Representation 2D vs. 3D (contd.) 2D Disadvantages Cannot handle pose variations Sensitive to lighting variations, shadows etc. 3D Disadvantages Cameras are still expensive Takes time to reconstruct models Unavailability of large collections of 3D data (UofY/ Cybula data set, U of Notre Dame data set)

11 Techniques Appearance based techniques Feature based Techniques Model based Techniques Eigen faces and Fisher faces Distances between landmark points such as eyes, nose and mouth. Graph matching techniques Active appearance/ shape models, Fitting morphable models

12 Eigen Analysis One of the most popular methods for face recognition The central argument is faces contain a lot of features some are common to all faces, some are highly discriminatory information. So they have to be mapped to different feature space that consists of discriminatory information a dimensionality reduction method is needed Eigen analysis provides a way to identify dimensions that indicate high variance - so we can use Eigen analysis to extract principal components

13 A simple example y = P x where y coordinates in the new space x coordinates in the previous space P projection matrix -a face

14 Eigen Faces projections of a face template along different principle components

15 Previous Work Using 2D images Sirovich and Kirby (1987), Turk and Pentland (1991) Using 3D images Heseltine, Pears and Austin (2003), Chang, Bowyer and Flynn (2003)

16 Linear Discriminant Analysis Subject A Subject B The aim is to minimise the within class separation and maximise between class separation. In other words, maximise the ratio between between class variance and within class variance Subject C Maximise (S B S -1 w ) Where S B between class scatter matrix S w within class scatter matrix

17 Previous Work Using 2D images Belhumeur, Hespanha and Kriegman (1997), Etemad and Chellappa (1996), Liu and Wiechsler (1998), Kittler (1999) Using 3D images Heseltine, Pears and Austin (2004)

18 Is LDA always better than PCA? PCA LDA D LDA D PCA Martinez and Kak (IEEE PAMI, 2001) Present experimental data to show that LDA does not always outperform PCA particularly when the number of samples in a class is small

19 Feature based matching techniques One of the earliest techniques is to use distance between landmarks such as eye, nose and mouth This technique may not be robust due to pose variations and it may be difficult to accurately identifying the required feature points

20 Cybula approach 3D graph matching A 3D mesh is used to identify a set of significant points we identify high curvature points on face profiles These points and the relationships between points are represented in a graph A graph matching framework called Relaxation by Elimination (RBE) developed at York is used.

21 Elastic Bunch Graph Matching But we are not the only people who have applied graph matching to faces! Wiskott, Fellous, Kruger and Malsburg (1999) have used graph matching for 2D face recognition. Each landmark point (eyes, mouth et.) is represented by a stack of wavelet responses. They become the nodes of the graph. Distances are represented in edges. Graph for a new image can be fitted by scaling, rotating and translating a standard model graph. Dissimilarity measure is a straight-forward comparison between graphs

22 Model based recognition Active appearance models (Cootes, Edwards and Taylor, 2001) A statistical appearance model is constructed by combining a shape model and a texture model. Shape model is constructed by identifying the positions of landmark points Texture model represent gray level intensities. Model parameters are identified by applying Eigen analysis. Recognition is an iterative process in which model parameters are adjusted to obtain the best match

23 3D morphable model (Blanz and Vetter, 2003) A set of laser scanned 3D image models (100 males and 100 females) are used to construct the morphable 3D model. Shape is represented by 3D co-ordinates while texture is represented by colour. Model parameters are calculated by applying Eigen analysis. 3D model is deformed to obtain the best fit between its 2D projection and the new 2D image. New model parameters are used to describe the new image. So this could be seen as 2D to 3D mapping Optimisation process involves finding out optimum values for model parameters as well as scene parameters (pose, focal length of the camera, light intensity, colour and direction)

24 One remaining issue how to keep the data collections updated? Face is changed when people become older and it could depend on both internal and external factors Lanitis, Taylor and Cootes (2002) have extended their work on active appearance model to predict the age of an unseen subject and then to simulate/ eliminate age effects Using training data, they build up a weighted person specific aging function to predict an age of a person using appearance as well as external factors such as lifestyle Age simulation can be done by changing the model parameters.

25 Evaluation False acceptance rate (FAR) number of times a wrong person is accepted False rejection rate (FRR) - number of times the correct person is rejected Equal error rates the value that FAR and FRR becomes equal Time to verify Time to capture/ enrol

26 Benchmark Assessments FRVT has been replaced by the Grand Challenge Experiment led by NIST First round was finished in this month the second round results submission is due next year

27

28

29

30

31

32

CHAPTER 5 SENDER AUTHENTICATION USING FACE BIOMETRICS

CHAPTER 5 SENDER AUTHENTICATION USING FACE BIOMETRICS 74 CHAPTER 5 SENDER AUTHENTICATION USING FACE BIOMETRICS 5.1 INTRODUCTION Face recognition has become very popular in recent years, and is used in many biometric-based security systems. Face recognition

More information

Advances in Face Recognition Research Second End-User Group Meeting - Feb 21, 2008 Dr. Stefan Gehlen, L-1 Identity Solutions AG, Bochum, Germany

Advances in Face Recognition Research Second End-User Group Meeting - Feb 21, 2008 Dr. Stefan Gehlen, L-1 Identity Solutions AG, Bochum, Germany Advances in Face Recognition Research Second End-User Group Meeting - Feb 21, 2008 Dr. Stefan Gehlen, L-1 Identity Solutions AG, Bochum, Germany L-1 Identity Solutions AG All rights reserved Outline Face

More information

FACE RECOGNITION TECHNOLOGY WHITE PAPER

FACE RECOGNITION TECHNOLOGY WHITE PAPER FACE RECOGNITION TECHNOLOGY WHITE PAPER Aug 2009 SCOPE FingerTec presented an automatic face recognition algorithm by combining 2D and 3D local features ensure accuracy and security when used as an authentication

More information

Adaptive Face Recognition System from Myanmar NRC Card

Adaptive Face Recognition System from Myanmar NRC Card Adaptive Face Recognition System from Myanmar NRC Card Ei Phyo Wai University of Computer Studies, Yangon, Myanmar Myint Myint Sein University of Computer Studies, Yangon, Myanmar ABSTRACT Biometrics is

More information

International Journal of Advanced Information in Arts, Science & Management Vol.2, No.2, December 2014

International Journal of Advanced Information in Arts, Science & Management Vol.2, No.2, December 2014 Efficient Attendance Management System Using Face Detection and Recognition Arun.A.V, Bhatath.S, Chethan.N, Manmohan.C.M, Hamsaveni M Department of Computer Science and Engineering, Vidya Vardhaka College

More information

Biometrics and the Cloud

Biometrics and the Cloud Biometrics and the Cloud CWI-CTiF Workshop on "Cloud communication and applications" Zheng-Hua Tan Aalborg University, Denmark CWI-CTiF Workshop 2011, Copenhagen 1 Outline Cloud security Biometrics Voice

More information

Designing and Testing an Anonymous Face Recognition System

Designing and Testing an Anonymous Face Recognition System Designing and Testing an Anonymous Face Recognition System Joris Diesvelt University of Twente P.O. Box 217, 7500AE Enschede The Netherlands j.j.diesvelt@student.utwente.nl ABSTRACT This paper contains

More information

Face Recognition using SIFT Features

Face Recognition using SIFT Features Face Recognition using SIFT Features Mohamed Aly CNS186 Term Project Winter 2006 Abstract Face recognition has many important practical applications, like surveillance and access control.

More information

May 2010. For other information please contact:

May 2010. For other information please contact: access control biometrics user guide May 2010 For other information please contact: British Security Industry Association t: 0845 389 3889 f: 0845 389 0761 e: info@bsia.co.uk www.bsia.co.uk Form No. 181.

More information

FACE RECOGNITION BASED ATTENDANCE MARKING SYSTEM

FACE RECOGNITION BASED ATTENDANCE MARKING SYSTEM Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 2, February 2014,

More information

A Mobile-based Face Verification System

A Mobile-based Face Verification System A Mobile-based Face Verification System Zongying Ou, Tieming Su, Fan Ou, Jianxin Zhang, Dianting Liu CAD CG & Network Lab. Dalian Uniersity of Technology, China E-mail ouzyg@dlut.edu.cn Abstract Intelligent

More information

Expression Invariant 3D Face Recognition with a Morphable Model

Expression Invariant 3D Face Recognition with a Morphable Model Expression Invariant 3D Face Recognition with a Morphable Model Brian Amberg brian.amberg@unibas.ch Reinhard Knothe reinhard.knothe@unibas.ch Thomas Vetter thomas.vetter@unibas.ch Abstract We present an

More information

Face Recognition Based on PCA Algorithm Using Simulink in Matlab

Face Recognition Based on PCA Algorithm Using Simulink in Matlab Face Recognition Based on PCA Algorithm Using Simulink in Matlab Dinesh Kumar 1, Rajni 2. 1 Mtech scholar department of ECE DCRUST Murthal Sonipat Haryana, 2 Assistant Prof. Department of ECE DCRUST Murthal

More information

LOCAL SURFACE PATCH BASED TIME ATTENDANCE SYSTEM USING FACE. indhubatchvsa@gmail.com

LOCAL SURFACE PATCH BASED TIME ATTENDANCE SYSTEM USING FACE. indhubatchvsa@gmail.com LOCAL SURFACE PATCH BASED TIME ATTENDANCE SYSTEM USING FACE 1 S.Manikandan, 2 S.Abirami, 2 R.Indumathi, 2 R.Nandhini, 2 T.Nanthini 1 Assistant Professor, VSA group of institution, Salem. 2 BE(ECE), VSA

More information

Efficient Attendance Management: A Face Recognition Approach

Efficient Attendance Management: A Face Recognition Approach Efficient Attendance Management: A Face Recognition Approach Badal J. Deshmukh, Sudhir M. Kharad Abstract Taking student attendance in a classroom has always been a tedious task faultfinders. It is completely

More information

Visual-based ID Verification by Signature Tracking

Visual-based ID Verification by Signature Tracking Visual-based ID Verification by Signature Tracking Mario E. Munich and Pietro Perona California Institute of Technology www.vision.caltech.edu/mariomu Outline Biometric ID Visual Signature Acquisition

More information

A Comparison of Photometric Normalisation Algorithms for Face Verification

A Comparison of Photometric Normalisation Algorithms for Face Verification A Comparison of Photometric Normalisation Algorithms for Face Verification James Short, Josef Kittler and Kieron Messer Centre for Vision, Speech and Signal Processing University of Surrey Guildford, Surrey,

More information

Illumination, Expression and Occlusion Invariant Pose-Adaptive Face Recognition System for Real- Time Applications

Illumination, Expression and Occlusion Invariant Pose-Adaptive Face Recognition System for Real- Time Applications Illumination, Expression and Occlusion Invariant Pose-Adaptive Face Recognition System for Real- Time Applications Shireesha Chintalapati #1, M. V. Raghunadh *2 Department of E and CE NIT Warangal, Andhra

More information

FACE RECOGNITION USING FEATURE EXTRACTION AND NEURO-FUZZY TECHNIQUES

FACE RECOGNITION USING FEATURE EXTRACTION AND NEURO-FUZZY TECHNIQUES International Journal of Electronics and Computer Science Engineering 2048 Available Online at www.ijecse.org ISSN : 2277-1956 FACE RECOGNITION USING FEATURE EXTRACTION AND NEURO-FUZZY TECHNIQUES Ritesh

More information

Multimodal Biometric Recognition Security System

Multimodal Biometric Recognition Security System Multimodal Biometric Recognition Security System Anju.M.I, G.Sheeba, G.Sivakami, Monica.J, Savithri.M Department of ECE, New Prince Shri Bhavani College of Engg. & Tech., Chennai, India ABSTRACT: Security

More information

Normalisation of 3D Face Data

Normalisation of 3D Face Data Normalisation of 3D Face Data Chris McCool, George Mamic, Clinton Fookes and Sridha Sridharan Image and Video Research Laboratory Queensland University of Technology, 2 George Street, Brisbane, Australia,

More information

3D Facial Image Comparison using Landmarks

3D Facial Image Comparison using Landmarks 3D Facial Image Comparison using Landmarks A study to the discriminating value of the characteristics of 3D facial landmarks and their automated detection. Alize Scheenstra Master thesis: INF/SCR-04-54

More information

Eyeglass Localization for Low Resolution Images

Eyeglass Localization for Low Resolution Images Eyeglass Localization for Low Resolution Images Earl Arvin Calapatia 1 1 De La Salle University 1 earl_calapatia@dlsu.ph Abstract: Facial data is a necessity in facial image processing technologies. In

More information

Face Model Fitting on Low Resolution Images

Face Model Fitting on Low Resolution Images Face Model Fitting on Low Resolution Images Xiaoming Liu Peter H. Tu Frederick W. Wheeler Visualization and Computer Vision Lab General Electric Global Research Center Niskayuna, NY, 1239, USA {liux,tu,wheeler}@research.ge.com

More information

Biometric Authentication using Online Signature

Biometric Authentication using Online Signature University of Trento Department of Mathematics Outline Introduction An example of authentication scheme Performance analysis and possible improvements Outline Introduction An example of authentication

More information

CHAPTER 1 INTRODUCTION

CHAPTER 1 INTRODUCTION 1 CHAPTER 1 INTRODUCTION 1.1 GENERAL The human authorization plays an important role in the security system. Every human consists of biometric features. The features that are extracted in the form of a

More information

A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition

A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition Computer Vision and Image Understanding 101 (2006) 1 15 www.elsevier.com/locate/cviu A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition Kevin W. Bowyer *, Kyong Chang,

More information

Statistics in Face Recognition: Analyzing Probability Distributions of PCA, ICA and LDA Performance Results

Statistics in Face Recognition: Analyzing Probability Distributions of PCA, ICA and LDA Performance Results Statistics in Face Recognition: Analyzing Probability Distributions of PCA, ICA and LDA Performance Results Kresimir Delac 1, Mislav Grgic 2 and Sonja Grgic 2 1 Croatian Telecom, Savska 32, Zagreb, Croatia,

More information

Image Normalization for Face Recognition using 3D Model

Image Normalization for Face Recognition using 3D Model Image Normalization for Face Recognition using 3D Model Zahid Riaz, Michael Beetz and Bernd Radig Abstract This paper describes an image segmentation and normalization technique using 3D point distribution

More information

Nonparametric Subspace Analysis for Face Recognition

Nonparametric Subspace Analysis for Face Recognition Nonparametric Subspace Analysis for Face Recognition Zhifeng Li 1,WeiLiu 1, Dahua Lin 1, and Xiaoou Tang 1, 2 1 Department of Information Engineering The Chinese University of Hong Kong, Shatin, Hong Kong

More information

THREE APPROACHES FOR FACE RECOGNITION

THREE APPROACHES FOR FACE RECOGNITION THREE APPROACHES FOR FACE RECOGNITION V.V. Starovoitov 1, D.I Samal 1, D.V. Briliuk 1 The face recognition problem is studied. Face normalization procedure is presented. Methods of face recognition such

More information

Mathematical Model Based Total Security System with Qualitative and Quantitative Data of Human

Mathematical Model Based Total Security System with Qualitative and Quantitative Data of Human Int Jr of Mathematics Sciences & Applications Vol3, No1, January-June 2013 Copyright Mind Reader Publications ISSN No: 2230-9888 wwwjournalshubcom Mathematical Model Based Total Security System with Qualitative

More information

HUMAN FACE DETECTION AND RECOGNITION

HUMAN FACE DETECTION AND RECOGNITION HUMAN FACE DETECTION AND RECOGNITION A THESIS SUBMITTED IN PARALLEL FULFULMENT OF THE REQUIREMENTS FOR THE DEGREE OF Bachelor in Technology In Electronics and Communication Engineering by K Krishan Kumar

More information

Resampling for Face Recognition

Resampling for Face Recognition Resampling for Face Recognition Xiaoguang Lu and Anil K. Jain Dept. of Computer Science & Engineering, Michigan State University East Lansing, MI 48824 {lvxiaogu,jain}@cse.msu.edu Abstract. A number of

More information

Panasonic Iris Reader The Biometric For Identity Management and Access Control

Panasonic Iris Reader The Biometric For Identity Management and Access Control Panasonic Iris Reader The Biometric For Identity Management and Access Control safe and non-intrusive. higher security. easy integration. Panasonic Iris Reader: Your Biometric Solution for Access Control

More information

SYMMETRIC EIGENFACES MILI I. SHAH

SYMMETRIC EIGENFACES MILI I. SHAH SYMMETRIC EIGENFACES MILI I. SHAH Abstract. Over the years, mathematicians and computer scientists have produced an extensive body of work in the area of facial analysis. Several facial analysis algorithms

More information

A Scheme of Human Face Recognition in Complex Environments

A Scheme of Human Face Recognition in Complex Environments A Scheme of Human Face Recognition in Complex Environments WEI CUI A thesis submitted to Auckland University of Technology in partial fulfilment of the requirements for the degree of Master of Computer

More information

A Various Biometric application for authentication and identification

A Various Biometric application for authentication and identification A Various Biometric application for authentication and identification 1 Karuna Soni, 2 Umesh Kumar, 3 Priya Dosodia, Government Mahila Engineering College, Ajmer, India Abstract: In today s environment,

More information

Multimodal Biometrics Based Integrated Authentication and Security System

Multimodal Biometrics Based Integrated Authentication and Security System Multimodal Biometrics Based Integrated Authentication and Security System Mr.Rajput Nirajsing 1, Ms. Rajgire Mayuri, 2 Mr. Shaikh Moinuddin 3, Ms.Nakhawa Priyanka 4 1 2 3 4 (Comp Dept & DYPCOE,,Talegaon,

More information

Face Recognition with Local Binary Patterns

Face Recognition with Local Binary Patterns Face Recognition with Local Binary Patterns Timo Ahonen, Abdenour Hadid, and Matti Pietikäinen Machine Vision Group, Infotech Oulu PO Box 4500, FIN-90014 University of Oulu, Finland, {tahonen,hadid,mkp}@ee.oulu.fi,

More information

A Comparative Study between PCA and SOM for Plastic Surgery Face Recognition

A Comparative Study between PCA and SOM for Plastic Surgery Face Recognition A Comparative Study between PCA and SOM for Plastic Surgery Face Recognition Sunita Roy 1, Prof. Samir K. Bandyopadhyay 2 1 Ph.D. Scholar in the Department of Computer Science & Engineering, University

More information

Development of Attendance Management System using Biometrics.

Development of Attendance Management System using Biometrics. Development of Attendance Management System using Biometrics. O. Shoewu, Ph.D. 1,2* and O.A. Idowu, B.Sc. 1 1 Department of Electronic and Computer Engineering, Lagos State University, Epe Campus, Nigeria.

More information

TIETS34 Seminar: Data Mining on Biometric identification

TIETS34 Seminar: Data Mining on Biometric identification TIETS34 Seminar: Data Mining on Biometric identification Youming Zhang Computer Science, School of Information Sciences, 33014 University of Tampere, Finland Youming.Zhang@uta.fi Course Description Content

More information

Why is Facial Occlusion a Challenging Problem?

Why is Facial Occlusion a Challenging Problem? Why is Facial Occlusion a Challenging Problem? Hazım Kemal Ekenel and Rainer Stiefelhagen Computer Science Depatment, Universität Karlsruhe (TH) Am Fasanengarten 5, Karlsruhe 76131, Germany {ekenel,stiefel}@ira.uka.de

More information

Image Compression Effects on Face Recognition for Images with Reduction in Size

Image Compression Effects on Face Recognition for Images with Reduction in Size Image Compression Effects on Face Recognition for Images with Reduction in Size Padmaja.V.K Jawaharlal Nehru Technological University, Anantapur Giri Prasad, PhD. B. Chandrasekhar, PhD. ABSTRACT In this

More information

Face detection is a process of localizing and extracting the face region from the

Face detection is a process of localizing and extracting the face region from the Chapter 4 FACE NORMALIZATION 4.1 INTRODUCTION Face detection is a process of localizing and extracting the face region from the background. The detected face varies in rotation, brightness, size, etc.

More information

Facial Biometric Templates and Aging: Problems and Challenges for Artificial Intelligence

Facial Biometric Templates and Aging: Problems and Challenges for Artificial Intelligence Facial Biometric Templates and Aging: Problems and Challenges for Artificial Intelligence Andreas Lanitis Department of Multimedia and Graphic Arts, Cyprus University of Technology P.O Box 50329, Lemesos,

More information

Automatic Pixel Selection for Optimizing Facial Expression Recognition using Eigenfaces

Automatic Pixel Selection for Optimizing Facial Expression Recognition using Eigenfaces Automatic Pixel Selection for Optimizing Facial Expression Recognition using Eigenfaces Lehrstuhl für Mustererkennung, Universität Erlangen-Nürnberg, Martensstraße 3, 91058 Erlangen, Germany, {frank,noeth}@informatik.uni-erlangen.de

More information

Face Recognition with Different Approaches: A Survey

Face Recognition with Different Approaches: A Survey International Journal of Emerging Engineering Research and Technology Volume 2, Issue 7, October 2014, PP 288-293 ISSN 2349-4395 (Print) & ISSN 2349-4409 (Online) Face Recognition with Different Approaches:

More information

Biometric Authentication using Online Signatures

Biometric Authentication using Online Signatures Biometric Authentication using Online Signatures Alisher Kholmatov and Berrin Yanikoglu alisher@su.sabanciuniv.edu, berrin@sabanciuniv.edu http://fens.sabanciuniv.edu Sabanci University, Tuzla, Istanbul,

More information

A comparative study on face recognition techniques and neural network

A comparative study on face recognition techniques and neural network A comparative study on face recognition techniques and neural network 1. Abstract Meftah Ur Rahman Department of Computer Science George Mason University mrahma12@masonlive.gmu.edu In modern times, face

More information

A Fully Automatic Approach for Human Recognition from Profile Images Using 2D and 3D Ear Data

A Fully Automatic Approach for Human Recognition from Profile Images Using 2D and 3D Ear Data A Fully Automatic Approach for Human Recognition from Profile Images Using 2D and 3D Ear Data S. M. S. Islam, M. Bennamoun, A. S. Mian and R. Davies School of Computer Science and Software Engineering,

More information

Principal Gabor Filters for Face Recognition

Principal Gabor Filters for Face Recognition Principal Gabor Filters for Face Recognition Vitomir Štruc, Rok Gajšek and Nikola Pavešić Abstract Gabor filters have proven themselves to be a powerful tool for facial feature extraction. An abundance

More information

Image Analysis for Face Recognition

Image Analysis for Face Recognition Image Analysis for Face Recognition Xiaoguang Lu Dept. of Computer Science & Engineering Michigan State University, East Lansing, MI, 48824 Email: lvxiaogu@cse.msu.edu Abstract In recent years face recognition

More information

IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 16, NO. 10, OCTOBER

IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 16, NO. 10, OCTOBER IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 16, NO. 10, OCTOBER 2007 2617 A Comparative Study of Local Matching Approach for Face Recognition Jie Zou, Member, IEEE, Qiang Ji, Senior Member, IEEE, and George

More information

Biometrics. By Alia Hassan and Ingy Nazif. Computer Science Research Paper, December 2013

Biometrics. By Alia Hassan and Ingy Nazif. Computer Science Research Paper, December 2013 Biometrics By and Ingy Nazif 900131729 900131781, December 2013 Biometrics Introduction As technology evolves, and our personal details become more and more susceptible to hacking, one of the most important

More information

Knowledge Visualization in Biometric Face Recognition on Twodimensional

Knowledge Visualization in Biometric Face Recognition on Twodimensional Knowledge Visualization in Biometric Face Recognition on Twodimensional Images Koruga Petra *, Baca Miroslav *, Fotak Tomislav * * Faculty of Organization and Informatics, Centre for Biometrics Pavlinska

More information

LITERATURE SURVEY OF AUTOMATIC FACE RECOGNITION SYSTEM AND EIGENFACE BASED IMPLEMENTATION. A Thesis

LITERATURE SURVEY OF AUTOMATIC FACE RECOGNITION SYSTEM AND EIGENFACE BASED IMPLEMENTATION. A Thesis 1 LITERATURE SURVEY OF AUTOMATIC FACE RECOGNITION SYSTEM AND EIGENFACE BASED IMPLEMENTATION A Thesis Submitted to the Department of Computer Science and Engineering of BRAC University by Md. Shariful Islam

More information

Facial Image Super Resolution Using Sparse Representation for Improving Face Recognition in Surveillance Monitoring

Facial Image Super Resolution Using Sparse Representation for Improving Face Recognition in Surveillance Monitoring Facial Image Super Resolution Using Sparse Representation for Improving Face Recognition in Surveillance Monitoring Tõnis Uiboupin Pejman Rasti (Head of Image Processing division of icv Group) Gholamreza

More information

Face Recognition Technology

Face Recognition Technology International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 10 (2013), pp. 1029-1034 International Research Publications House http://www. irphouse.com /ijict.htm Face

More information

ECE 533 Project Report Ashish Dhawan Aditi R. Ganesan

ECE 533 Project Report Ashish Dhawan Aditi R. Ganesan Handwritten Signature Verification ECE 533 Project Report by Ashish Dhawan Aditi R. Ganesan Contents 1. Abstract 3. 2. Introduction 4. 3. Approach 6. 4. Pre-processing 8. 5. Feature Extraction 9. 6. Verification

More information

Biometrics Technology and Standards Overview

Biometrics Technology and Standards Overview Biometrics Technology and Standards Overview Biometrics General term used alternatively to describe a characteristic or a process As a Characteristic it is a measurable biological (anatomical and physiological)

More information

Face Recognition: Some Challenges in Forensics. Anil K. Jain, Brendan Klare, and Unsang Park

Face Recognition: Some Challenges in Forensics. Anil K. Jain, Brendan Klare, and Unsang Park Face Recognition: Some Challenges in Forensics Anil K. Jain, Brendan Klare, and Unsang Park Forensic Identification Apply A l science i tto analyze data for identification Traditionally: Latent FP, DNA,

More information

Recognition of Occluded Faces Using an Enhanced EBGM Algorithm.

Recognition of Occluded Faces Using an Enhanced EBGM Algorithm. Recognition of Occluded Faces Using an Enhanced EBGM Algorithm Badr Mohammed Lahasan Email: bmo12 com009@studentusmmy Ibrahim Venkat Email: ibrahim@csusmmy Syaheerah Lebai Lutfi Email: syaheerah@csusmmy

More information

Multi-Factor Biometrics: An Overview

Multi-Factor Biometrics: An Overview Multi-Factor Biometrics: An Overview Jones Sipho-J Matse 24 November 2014 1 Contents 1 Introduction 3 1.1 Characteristics of Biometrics........................ 3 2 Types of Multi-Factor Biometric Systems

More information

Physical Security: A Biometric Approach Preeti, Rajni M.Tech (Network Security),BPSMV preetytushir@gmail.com, ratri451@gmail.com

Physical Security: A Biometric Approach Preeti, Rajni M.Tech (Network Security),BPSMV preetytushir@gmail.com, ratri451@gmail.com www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 3 Issue 2 February, 2014 Page No. 3864-3868 Abstract: Physical Security: A Approach Preeti, Rajni M.Tech (Network

More information

Access control: adaptation and real time implantation of a face recognition method.

Access control: adaptation and real time implantation of a face recognition method. Access control: adaptation and real time implantation of a face recognition method. J. Mitéran, J.P. Zimmer, F. Yang, M. Paindavoine. Laboratory Le2i, University of Burgundy, Aile des Sciences de l'ingénieur,

More information

Method of Combining the Degrees of Similarity in Handwritten Signature Authentication Using Neural Networks

Method of Combining the Degrees of Similarity in Handwritten Signature Authentication Using Neural Networks Method of Combining the Degrees of Similarity in Handwritten Signature Authentication Using Neural Networks Ph. D. Student, Eng. Eusebiu Marcu Abstract This paper introduces a new method of combining the

More information

Digital Identity & Authentication Directions Biometric Applications Who is doing what? Academia, Industry, Government

Digital Identity & Authentication Directions Biometric Applications Who is doing what? Academia, Industry, Government Digital Identity & Authentication Directions Biometric Applications Who is doing what? Academia, Industry, Government Briefing W. Frisch 1 Outline Digital Identity Management Identity Theft Management

More information

3M Cogent, Inc. White Paper. Facial Recognition. Biometric Technology. a 3M Company

3M Cogent, Inc. White Paper. Facial Recognition. Biometric Technology. a 3M Company 3M Cogent, Inc. White Paper Facial Recognition Biometric Technology a 3M Company Automated Facial Recognition: Turning Promise Into Reality Once the province of fiction, automated facial recognition has

More information

Object Recognition and Template Matching

Object Recognition and Template Matching Object Recognition and Template Matching Template Matching A template is a small image (sub-image) The goal is to find occurrences of this template in a larger image That is, you want to find matches of

More information

Online Learning in Biometrics: A Case Study in Face Classifier Update

Online Learning in Biometrics: A Case Study in Face Classifier Update Online Learning in Biometrics: A Case Study in Face Classifier Update Richa Singh, Mayank Vatsa, Arun Ross, and Afzel Noore Abstract In large scale applications, hundreds of new subjects may be regularly

More information

Face Recognition: Some Challenges in Forensics

Face Recognition: Some Challenges in Forensics To appear in the 9th IEEE Int'l Conference on Automatic Face and Gesture Recognition, Santa Barbara, CA, March, 2011. Face Recognition: Some Challenges in Forensics Anil K. Jain, Brendan Klare and Unsang

More information

Feature Extraction and Selection. More Info == Better Performance? Curse of Dimensionality. Feature Space. High-Dimensional Spaces

Feature Extraction and Selection. More Info == Better Performance? Curse of Dimensionality. Feature Space. High-Dimensional Spaces More Info == Better Performance? Feature Extraction and Selection APR Course, Delft, The Netherlands Marco Loog Feature Space Curse of Dimensionality A p-dimensional space, in which each dimension is a

More information

Framework for Biometric Enabled Unified Core Banking

Framework for Biometric Enabled Unified Core Banking Proc. of Int. Conf. on Advances in Computer Science and Application Framework for Biometric Enabled Unified Core Banking Manohar M, R Dinesh and Prabhanjan S Research Candidate, Research Supervisor, Faculty

More information

A secure face tracking system

A secure face tracking system International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 10 (2014), pp. 959-964 International Research Publications House http://www. irphouse.com A secure face tracking

More information

ICA-BASED LIP FEATURE REPRESENTATION FOR SPEAKER AUTHENTICATION

ICA-BASED LIP FEATURE REPRESENTATION FOR SPEAKER AUTHENTICATION Third International IEEE Conference on Signal-Image Technologies technologies and Internet-Based System ICA-BASED LIP FEATURE REPRESENTATION FOR SPEAKER AUTHENTICATION S.L.Wang and A. W. C. Liew # School

More information

Classification of Fingerprints. Sarat C. Dass Department of Statistics & Probability

Classification of Fingerprints. Sarat C. Dass Department of Statistics & Probability Classification of Fingerprints Sarat C. Dass Department of Statistics & Probability Fingerprint Classification Fingerprint classification is a coarse level partitioning of a fingerprint database into smaller

More information

Information Fusion in Low-Resolution Iris Videos using Principal Components Transform

Information Fusion in Low-Resolution Iris Videos using Principal Components Transform Information Fusion in Low-Resolution Iris Videos using Principal Components Transform Raghavender Jillela, Arun Ross West Virginia University {Raghavender.Jillela, Arun.Ross}@mail.wvu.edu Patrick J. Flynn

More information

Haar Features Based Face Detection and Recognition for Advanced Classroom and Corporate Attendance

Haar Features Based Face Detection and Recognition for Advanced Classroom and Corporate Attendance Haar Features Based Face Detection and Recognition for Advanced Classroom and Corporate Attendance Tiwari Priti Anilkumar 1, Kalyani Jha 2, Karishma P Uchil 3, Naveen H 4 U.G. Student, Dept. of ECE, M

More information

Subspace Analysis and Optimization for AAM Based Face Alignment

Subspace Analysis and Optimization for AAM Based Face Alignment Subspace Analysis and Optimization for AAM Based Face Alignment Ming Zhao Chun Chen College of Computer Science Zhejiang University Hangzhou, 310027, P.R.China zhaoming1999@zju.edu.cn Stan Z. Li Microsoft

More information

Palmprint Recognition. By Sree Rama Murthy kora Praveen Verma Yashwant Kashyap

Palmprint Recognition. By Sree Rama Murthy kora Praveen Verma Yashwant Kashyap Palmprint Recognition By Sree Rama Murthy kora Praveen Verma Yashwant Kashyap Palm print Palm Patterns are utilized in many applications: 1. To correlate palm patterns with medical disorders, e.g. genetic

More information

Accessing the bank account without card and password in ATM using biometric technology

Accessing the bank account without card and password in ATM using biometric technology Accessing the bank account without card and password in ATM using biometric technology Mini Agarwal [1] and Lavesh Agarwal [2] Teerthankar Mahaveer University Email: miniagarwal21@gmail.com [1], lavesh_1071985@yahoo.com

More information

Towards Face Unlock: On the Difficulty of Reliably Detecting Faces on Mobile Phones

Towards Face Unlock: On the Difficulty of Reliably Detecting Faces on Mobile Phones Towards Face Unlock: On the Difficulty of Reliably Detecting Faces on Mobile Phones ABSTRACT Rainhard D. Findling Department for Mobile Computing Upper Austria University of Applied Sciences Softwarepark

More information

Assignment 1 Biometric authentication

Assignment 1 Biometric authentication Assignment 1 Biometric authentication Internet Security and Privacy Alexandre Fustier Vincent Burger INTRODUCTION:...3 I. TYPES AND DESCRIPTION OF BIOMETRICS...4 1. PHYSIOLOGICAL BIOMETRIC...4 a. Fingerprints...4

More information

Biometrics in Physical Access Control Issues, Status and Trends White Paper

Biometrics in Physical Access Control Issues, Status and Trends White Paper Biometrics in Physical Access Control Issues, Status and Trends White Paper Authored and Presented by: Bill Spence, Recognition Systems, Inc. SIA Biometrics Industry Group Vice-Chair & SIA Biometrics Industry

More information

MULTIMODAL BIOMETRICS IN IDENTITY MANAGEMENT

MULTIMODAL BIOMETRICS IN IDENTITY MANAGEMENT International Journal of Information Technology and Knowledge Management January-June 2012, Volume 5, No. 1, pp. 111-115 MULTIMODAL BIOMETRICS IN IDENTITY MANAGEMENT A. Jaya Lakshmi 1, I. Ramesh Babu 2,

More information

PASSIVE DRIVER GAZE TRACKING WITH ACTIVE APPEARANCE MODELS

PASSIVE DRIVER GAZE TRACKING WITH ACTIVE APPEARANCE MODELS PASSIVE DRIVER GAZE TRACKING WITH ACTIVE APPEARANCE MODELS Takahiro Ishikawa Research Laboratories, DENSO CORPORATION Nisshin, Aichi, Japan Tel: +81 (561) 75-1616, Fax: +81 (561) 75-1193 Email: tishika@rlab.denso.co.jp

More information

Face Recognition: A Literature Review

Face Recognition: A Literature Review Face Recognition: A Literature Review A. S. Tolba, A.H. El-Baz, and A.A. El-Harby Abstract The task of face recognition has been actively researched in recent years. This paper provides an up-to-date review

More information

Physiological Biometric Authentication Systems, Advantages, Disadvantages And Future Development: A Review

Physiological Biometric Authentication Systems, Advantages, Disadvantages And Future Development: A Review Physiological Biometric Authentication Systems, Advantages, Disadvantages And Future Development: A Review Israa M. Alsaadi Abstract: With the fast increasing of the electronic crimes and their related

More information

Excursion Report. Applications of Image Processing Winter semester 08/09. Lecturer: Robert Sablating, Univ.-Prof. Tutor : Markus Diem

Excursion Report. Applications of Image Processing Winter semester 08/09. Lecturer: Robert Sablating, Univ.-Prof. Tutor : Markus Diem Excursion Report Applications of Image Processing Winter semester 08/09 Lecturer: Robert Sablating, Univ.-Prof. Tutor : Markus Diem Contents AIP - Excursion Introduction Baggage handling at Vienna airport

More information

REPORT DOCUMENTATION PAGE

REPORT DOCUMENTATION PAGE REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188 Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions,

More information

Capacity of an RCE-based Hamming Associative Memory for Human Face Recognition

Capacity of an RCE-based Hamming Associative Memory for Human Face Recognition Capacity of an RCE-based Hamming Associative Memory for Human Face Recognition Paul Watta Department of Electrical & Computer Engineering University of Michigan-Dearborn Dearborn, MI 48128 watta@umich.edu

More information

Expression-Invariant Multispectral Face Recognition: You Can Smile Now!

Expression-Invariant Multispectral Face Recognition: You Can Smile Now! Expression-Invariant Multispectral Face Recognition: You Can Smile Now! Ioannis A. Kakadiaris a, George Passalis a, George Toderici a, Yunliang Lu a, Nikos Karampatziakis a, Najam Murtuza a, Theoharis

More information

Face Recognition Across Time Lapse: On Learning Feature Subspaces

Face Recognition Across Time Lapse: On Learning Feature Subspaces Face Recognition Across Time Lapse: On Learning Feature Subspaces Brendan Klare and Anil K. Jain Dept. of Computer Science and Engineering Michigan State University East Lansing, MI, U.S.A. {klarebre,jain}@cse.msu.edu

More information

Index Terms: Face Recognition, Face Detection, Monitoring, Attendance System, and System Access Control.

Index Terms: Face Recognition, Face Detection, Monitoring, Attendance System, and System Access Control. Modern Technique Of Lecture Attendance Using Face Recognition. Shreya Nallawar, Neha Giri, Neeraj Deshbhratar, Shamal Sane, Trupti Gautre, Avinash Bansod Bapurao Deshmukh College Of Engineering, Sewagram,

More information

KEYSTROKE DYNAMIC BIOMETRIC AUTHENTICATION FOR WEB PORTALS

KEYSTROKE DYNAMIC BIOMETRIC AUTHENTICATION FOR WEB PORTALS KEYSTROKE DYNAMIC BIOMETRIC AUTHENTICATION FOR WEB PORTALS Plurilock Security Solutions Inc. www.plurilock.com info@plurilock.com 2 H IGHLIGHTS: PluriPass is Plurilock static keystroke dynamic biometric

More information

Sketch to Photo Matching: A Feature-based Approach

Sketch to Photo Matching: A Feature-based Approach Sketch to Photo Matching: A Feature-based Approach Brendan Klare a and Anil K Jain a,b a Department of Computer Science and Engineering Michigan State University East Lansing, MI, U.S.A b Department of

More information

Computer Vision - part II

Computer Vision - part II Computer Vision - part II Review of main parts of Section B of the course School of Computer Science & Statistics Trinity College Dublin Dublin 2 Ireland www.scss.tcd.ie Lecture Name Course Name 1 1 2

More information