CHAPTER 5 SENDER AUTHENTICATION USING FACE BIOMETRICS


 Sophia Poole
 2 years ago
 Views:
Transcription
1 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 biometricbased security systems. Face recognition is a general topic that includes both face identification and face authentication or verification (Mohamed et al 2010). On one hand, face authentication is concerned with validating a claimed identity based on the image of a face, and either accepting or rejecting the identity claim. On the other, the goal of face identification is to identify a person based on the image of a face. This face image has to be compared with all the registered persons. Thus, the key issues in face recognition are to extract the meaningful features that characterize a human face, and then recognize it. Some facial recognition algorithms identify faces by extracting landmarks, or features, from an image of the subject's face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. These features are then used to search for other images with matching features. Other algorithms normalize a gallery of face images and then compress the face data, saving only the data in the image that is useful for face detection. A probe image is then compared with the face data. Popular recognition algorithms include Principal Component Analysis (Turk and Pentland 1991 and Abdi and Williams 2010) using Eigen faces, Linear Discriminate Analysis (Martinez and Kak 2001), Elastic Bunch Graph Matching (Seiichi and Hiroaki 2005) using the Fisher face algorithm
2 75 (Peter et al 1997), and the neuronal motivated dynamic link matching ( In this work, the Eigen face based recognition algorithm is used along with the color based technique for face detection for sender authentication. These face detection algorithm and Eigen face based face recognition algorithm, are explained in the following sections. 5.2 DETECTION AND PREPROCESSING OF A FACE The first step of any fully automatic facial recognition system is face detection. Face detection (Prem et al 2002) is the process of finding whether or not there are any faces in a given image, and if present, the location and content of the face image are returned. Real world images need not necessarily contain isolated faces that can directly serve as inputs to a face recognition system, and hence, there is a need to isolate or segment facial regions from the background. A color based technique is implemented for detecting human faces in images. The method consists of two image processing steps. First, the skin regions are separated from the nonskin regions. After that, the human face within the skin regions is located and cropped. In order to segment the human skin regions from the nonskin regions based on color, a reliable skin color model of different people is needed ( ~robles/ee368/main.html). Luminance can be removed from the RGB color representation in chromatic color space. Chromatic colors such as Red (r), Blue (b) and Green (g) are defined by a normalization process as given in the Equation (5.1): r = R/(R+G+B) and b = B/(R+G+B) (5.1) where R, G and B are the red, green, and blue components of the image before color balancing, r, g and b are the color balanced red, green, and blue components of a pixel in the image, and the green color is redundant after the
3 76 normalization because r + g + b = 1. Our system uses a face library of sixteen facial images as shown in Figure 5.1 and these images are taken under controlled environment. Hundred and sixty skin samples from these sixteen color images are used to determine the color distribution of the human skin in chromatic color space. The color samples are shown in Figure 5.2. As the skin samples are extracted from the color images, the skin samples are filtered using a lowpass filter, to reduce the effect of noise in the samples. Figure 5.3 shows the color distribution of these skin samples in chromatic color space. Figure 5.1 Sample Face Image Database
4 77 Figure 5.2 Color Samples from the Face Image Database Figure 5.3 Color Distributions for Skincolor of Different People The color histogram revealed that the distributions of the skincolor of different people are clustered in chromatic color space, and a skin color distribution can be represented by a Gaussian model N (m, C), where: Mean, m = E {x} where x = (r, b) T (5.2) Covariance, C = E {(x  m) (x  m) T } (5.3) Figure 5.4. The Gaussian distribution, N (m, C) fitted by our data is shown in
5 78 With this Gaussian fitted skin color model, the likelihood of skin is obtained for any pixel of an image. If a pixel, having transformed from RGB color space to chromatic color space, has a chromatic pair value of (r, b), the likelihood of skin for this pixel can be computed as follows: Likelihood = P (r, b) = exp [0.5 (xm) T C 1 (xm)] (5.4) where x = (r, b) T, m = mean, C = Covariance, r = red and b = blue Figure 5.4 Gaussian Distribution from Skin Color Hence, this skin color model can transform a color image into a gray scale image such that the gray value at each pixel shows the likelihood of the pixel belonging to the skin. The skinlikelihood image will be a grayscale image whose gray values represent the likelihood of the pixel belonging to a skin. Since the skin regions are brighter than the other parts of the images, the skin segmentation can be done from the rest of the image through a
6 79 thresholding process. Since people with different skins have different likelihoods, an adaptive thresholding process is required to achieve the optimal threshold value for each image. Using this technique, the skincolored regions are effectively segmented from the nonskin colored regions. A skin region is defined as a closed region or a set of connected components within the image, which can have 0, 1 or more holes inside it. Its color boundary is represented by pixels with value 1 for binary images. All holes in a binary image have pixel value of zero (black). A sample color image, its resulting skinlikelihood image, the skin segmented image and the actual skin region are shown in Figure 5.5.a, b, c and d. a b c d Figure 5.5 Processing of the Original Face Image to get the Actual Skin Region To study the face region, its area and center are to be determined first. One efficient way is to compute the center of mass (i.e., centroid) of the region (Zhili and Chunhung 2006). The center of the area in binary images is the same as the center of the mass, and it is computed as shown below: n m 1 x j B [i, j] A i 1 j 1 (5.5) n m 1 y i B [i, j] A i 1 j 1 (5.6)
7 80 where B is the matrix of the size [n x m] representation of the region, and A is the area in pixels of the region. In that way, the center point (x, y) of the actual face region is found and is shown in Figure 5.6.a and b. a b Figure 5.6 Center Point in the Skin Region and in the Original Image Finally, using this center point the actual face region is cropped, normalized, and is shown in Figure 5.7. By using the same method, all the sample images are cropped and normalized, and are shown in Figure 5.8. Figure 5.7 Cropped and Normalized Face Region
8 81 Figure 5.8 Cropped and Normalized Face Images 5.3 EIGEN FACE CREATION Biometric authentication systems such as face recognition systems, are being actively investigated for access control and security applications. However, there are many issues that need to be addressed to ensure the security of biometric templates. One such aspect is the cancelability or revocability of a biometric (Marios et al 2004). In order to protect the user s
9 82 biometric templates from possible hacking and to ensure cancelability, the templates have to be changed in their form by applying a transformation function. Then in case of theft or loss, a different biometric template can be issued from the same original biometric by applying a different function. Eigen faces are a set of eigenvectors used for human face recognition. A set of Eigen faces can be generated by performing a mathematical process, called the Principal Component Analysis (PCA) (Turk and Pentland and Ramesh et al 1995) on a large set of human face images. The Eigen faces will appear as light and dark areas that are arranged in a specific pattern. This pattern shows how different features of a face are singled out to be evaluated and scored. The Eigen face approach is applied to the facial images of our database to recognize someone's face. The problem is to be able to accurately recognize a person's identity and allow the person to access highly secure information. The procedure involved in Eigen face creation is explained as follows: The cropped and normalized images are placed into the training set S and are resampled to the same pixel resolution. Each image is treated as one vector, simply by concatenating the rows of pixels in the original image, resulting in a single row with r c elements. The facial images shown in Figure 5.8 are converted into gray images, resampled and are used as the input for this method. In our example M = 16 and all images are transformed into a vector of size N and placed into the set S. S = { 1, 2, 3,... M} (5.7) and The training set and the normalized set are shown in Figures 5.9
10 83 Figure 5.9 Training Set Gray image Figure 5.10 Normalized Training Set
11 84 After that, the mean image is created by using the following Equation (5.8) and is shown in Figure M m n 1 n (5.8) Figure 5.11 Mean Image Then the difference between the input image and the mean image is found by using the Equation (5.9) i = i  (5.9) Next a set of M orthonormal vectors, u n, is found, which best describes the distribution of the data. The k th vector, u k, is chosen such that k 1 M m T 2 (u n 1 k n) (5.10) is the maximum, subject to u u T l k lk 1 if l k 0 otherwise u k and k are the Eigenvectors and Eigenvalues of the covariance matrix C and is calculated by using the Equation (5.11)
12 85 C 1 M m T T n 1 n n AA where A={ 1, 2,... n } (5.11) Since the C matrix is an N 2 x N 2 matrix, computing its eigenvectors is not computationally feasible. Instead, the eigenvectors v l of the new matrix L = A T A are found, which has the same eigenvectors of the matrix C = AA T L mn = T m n (5.12) Once the eigenvectors, v l, of the L matrix are found, the Eigen faces u l can also be found by the following Equation (5.13) and the Eigen faces of the original images are shown in Figure m u v l 1...,M (5.13) l lk k k 1 Figure 5.12 Eigen Faces
13 EIGEN FACE RECOGNITION SYSTEM At the receiving end, from the received facial image of the sender, the Eigen face component is generated, and it needs to be verified by the receiver, as to whether the Eigen face belongs to the given training set or not. In that way the receiver ensures that the message is sent only by the genuine sender, and not by a deceitful one. The recognition system involves the following steps: 1. First the Eigen face component is compared with the mean image and their difference is multiplied by each eigenvector. Each value would represent a weight, and would be saved on a vector. k u ( ) (5.14) T k where = weight, = eigenvector, = input image, = mean face. The weight vector is given by T [ 1, 2,..., M ] (5.15) 2. Then the face class which provides the best description for the input image is determined by minimizing the Euclidean distance k 2 k (5.16) 3. So, for a new face input, say k to be verified, the average weight vector, k th facial image s weight vector k and the Euclidean distance for this k th face k are found.
14 87 4. If this Euclidean distance k is less than an established threshold value, then the face image is considered to be a known face and it belongs to the training class. If the distance is above the given threshold, but below a second threshold, the image can be determined as an unknown face. If the input image is above these two thresholds, the image is determined not to be a face (Turk and Pentland 1991). 5.5 IMAGE RANDOMIZATION The facial images are collected at the leader of the group and once the mean image is found out, it is distributed to all the users. The same is done to all the leaders also. When user1 of group A sends data to his leader, the facial image of user1 is randomized using the image randomizer. Then, these randomized images are appended with the plain text, and sent to the receiver. In our work, the sender s facial image is divided into 16 pieces, and is randomized as shown in Figure At the receiving end, the decrypted images are reassembled and verified with the mean image. Figure 5.13 Sender s Facial Image is Divided and Randomized
15 RESULTS In this experiment, the threshold values of the Euclidean distance of a different number of faces are tested. The face library that is used for our experiment contains 32 face images in which 16 are genuine users images, and 16 are fake users images. All 16 genuine images are in the training set. Then, the Euclidean distance of every image of the face library is found out, and Figure 5.14 is plotted using these Euclidean distances. Figure 5.14 Recognition of the Genuine Users using the Euclidean Distance In Figure 5.14, the genuine users Euclidean distances are shown in blue color, and the fake users Euclidean distances are shown in red. It is clearly seen that all the genuine users images have Euclidean distances less than the threshold value of 300. It is also seen that all 16 genuine users are classified correctly, and all 16 fake users are not classified by this method. Though the system is tested for 16 users it can be extended for any number of users. Chapter 6 describes the preprocessing of voice biometrics and Euclidean distance generation. The chapter also explains how voice biometrics is used for receiver authorization in hierarchical MANETs.
Face Recognition using Principle Component Analysis
Face Recognition using Principle Component Analysis Kyungnam Kim Department of Computer Science University of Maryland, College Park MD 20742, USA Summary This is the summary of the basic idea about PCA
More informationFace 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 informationEfficient 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 informationMathematical Model Based Total Security System with Qualitative and Quantitative Data of Human
Int Jr of Mathematics Sciences & Applications Vol3, No1, JanuaryJune 2013 Copyright Mind Reader Publications ISSN No: 22309888 wwwjournalshubcom Mathematical Model Based Total Security System with Qualitative
More informationPCA to Eigenfaces. CS 510 Lecture #16 March 23 th A 9 dimensional PCA example
PCA to Eigenfaces CS 510 Lecture #16 March 23 th 2015 A 9 dimensional PCA example is dark around the edges and bright in the middle. is light with dark vertical bars. is light with dark horizontal bars.
More informationObject Recognition and Template Matching
Object Recognition and Template Matching Template Matching A template is a small image (subimage) The goal is to find occurrences of this template in a larger image That is, you want to find matches of
More informationAdaptive 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 informationBEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES
BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES 123 CHAPTER 7 BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES 7.1 Introduction Even though using SVM presents
More informationSIGNATURE VERIFICATION
SIGNATURE VERIFICATION Dr. H.B.Kekre, Dr. Dhirendra Mishra, Ms. Shilpa Buddhadev, Ms. Bhagyashree Mall, Mr. Gaurav Jangid, Ms. Nikita Lakhotia Computer engineering Department, MPSTME, NMIMS University
More informationInternational 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 informationAuthentication Scheme for ATM Based On Biometric K. Kavitha, IIMCA IFET COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS
Authentication Scheme for ATM Based On Biometric K. Kavitha, IIMCA IFET COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS ABSTRACT: Biometrics based authentication is a potential candidate to
More informationVolume 2, Issue 9, September 2014 International Journal of Advance Research in Computer Science and Management Studies
Volume 2, Issue 9, September 2014 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online at: www.ijarcsms.com
More informationLecture 9: Introduction to Pattern Analysis
Lecture 9: Introduction to Pattern Analysis g Features, patterns and classifiers g Components of a PR system g An example g Probability definitions g Bayes Theorem g Gaussian densities Features, patterns
More informationTIETS34 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 informationEM Clustering Approach for MultiDimensional Analysis of Big Data Set
EM Clustering Approach for MultiDimensional Analysis of Big Data Set Amhmed A. Bhih School of Electrical and Electronic Engineering Princy Johnson School of Electrical and Electronic Engineering Martin
More informationColor to Grayscale Conversion with Chrominance Contrast
Color to Grayscale Conversion with Chrominance Contrast Yuting Ye University of Virginia Figure 1: The sun in Monet s Impression Sunrise has similar luminance as the sky. It can hardly be seen when the
More informationReview Jeopardy. Blue vs. Orange. Review Jeopardy
Review Jeopardy Blue vs. Orange Review Jeopardy Jeopardy Round Lectures 03 Jeopardy Round $200 How could I measure how far apart (i.e. how different) two observations, y 1 and y 2, are from each other?
More informationLOCAL 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 informationA secure face tracking system
International Journal of Information & Computation Technology. ISSN 09742239 Volume 4, Number 10 (2014), pp. 959964 International Research Publications House http://www. irphouse.com A secure face tracking
More informationCanny Edge Detection
Canny Edge Detection 09gr820 March 23, 2009 1 Introduction The purpose of edge detection in general is to significantly reduce the amount of data in an image, while preserving the structural properties
More informationEnvironmental Remote Sensing GEOG 2021
Environmental Remote Sensing GEOG 2021 Lecture 4 Image classification 2 Purpose categorising data data abstraction / simplification data interpretation mapping for land cover mapping use land cover class
More informationBiometric 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 informationPrincipal components analysis
CS229 Lecture notes Andrew Ng Part XI Principal components analysis In our discussion of factor analysis, we gave a way to model data x R n as approximately lying in some kdimension subspace, where k
More information2.11 CMC curves showing the performance of sketch to digital face image matching. algorithms on the CUHK database... 40
List of Figures 1.1 Illustrating different stages in a face recognition system i.e. image acquisition, face detection, face normalization, feature extraction, and matching.. 10 1.2 Illustrating the concepts
More informationSignature verification using KolmogorovSmirnov. statistic
Signature verification using KolmogorovSmirnov statistic Harish Srinivasan, Sargur N.Srihari and Matthew J Beal University at Buffalo, the State University of New York, Buffalo USA {srihari,hs32}@cedar.buffalo.edu,mbeal@cse.buffalo.edu
More informationComponent Ordering in Independent Component Analysis Based on Data Power
Component Ordering in Independent Component Analysis Based on Data Power Anne Hendrikse Raymond Veldhuis University of Twente University of Twente Fac. EEMCS, Signals and Systems Group Fac. EEMCS, Signals
More informationBlind Deconvolution of Barcodes via Dictionary Analysis and Wiener Filter of Barcode Subsections
Blind Deconvolution of Barcodes via Dictionary Analysis and Wiener Filter of Barcode Subsections Maximilian Hung, Bohyun B. Kim, Xiling Zhang August 17, 2013 Abstract While current systems already provide
More informationMugshot Identification from Manipulated Facial Images Chennamma H.R.* and Lalitha Rangarajan
Mugshot Identification from Manipulated Facial Images Chennamma H.R.* and Lalitha Rangarajan Dept. Of Studies in Computer Science, University of Mysore, Mysore, INDIA Anusha_hr@rediffmail.com, lali85arun@yahoo.co.in
More informationDESIGN OF DIGITAL SIGNATURE VERIFICATION ALGORITHM USING RELATIVE SLOPE METHOD
DESIGN OF DIGITAL SIGNATURE VERIFICATION ALGORITHM USING RELATIVE SLOPE METHOD P.N.Ganorkar 1, Kalyani Pendke 2 1 Mtech, 4 th Sem, Rajiv Gandhi College of Engineering and Research, R.T.M.N.U Nagpur (Maharashtra),
More informationDEVELOPING AN IMAGE RECOGNITION ALGORITHM FOR FACIAL AND DIGIT IDENTIFICATION
DEVELOPING AN IMAGE RECOGNITION ALGORITHM FOR FACIAL AND DIGIT IDENTIFICATION ABSTRACT Christian Cosgrove, Kelly Li, Rebecca Lin, Shree Nadkarni, Samanvit Vijapur, Priscilla Wong, Yanjun Yang, Kate Yuan,
More informationPalmprint as a Biometric Identifier
Palmprint as a Biometric Identifier 1 Kasturika B. Ray, 2 Rachita Misra 1 Orissa Engineering College, Nabojyoti Vihar, Bhubaneswar, Orissa, India 2 Dept. Of IT, CV Raman College of Engineering, Bhubaneswar,
More informationA Matlab Project in Optical Character Recognition (OCR)
A Matlab Project in Optical Character Recognition (OCR) Jesse Hansen Introduction: What is OCR? The goal of Optical Character Recognition (OCR) is to classify optical patterns (often contained in a digital
More informationPATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION
PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION Introduction In the previous chapter, we explored a class of regression models having particularly simple analytical
More informationA New Robust Algorithm for Video Text Extraction
A New Robust Algorithm for Video Text Extraction Pattern Recognition, vol. 36, no. 6, June 2003 Edward K. Wong and Minya Chen School of Electrical Engineering and Computer Science Kyungpook National Univ.
More information3)Skilled Forgery: It is represented by suitable imitation of genuine signature mode.it is also called WellVersed Forgery[4].
Volume 4, Issue 7, July 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A New Technique
More informationStatistics 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 informationMachine Learning for Data Science (CS4786) Lecture 1
Machine Learning for Data Science (CS4786) Lecture 1 TuTh 10:10 to 11:25 AM Hollister B14 Instructors : Lillian Lee and Karthik Sridharan ROUGH DETAILS ABOUT THE COURSE Diagnostic assignment 0 is out:
More informationLecture 4: Thresholding
Lecture 4: Thresholding c Bryan S. Morse, Brigham Young University, 1998 2000 Last modified on Wednesday, January 12, 2000 at 10:00 AM. Reading SH&B, Section 5.1 4.1 Introduction Segmentation involves
More informationMultimodal 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 informationDesigning 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 informationLocating and Decoding EAN13 Barcodes from Images Captured by Digital Cameras
Locating and Decoding EAN13 Barcodes from Images Captured by Digital Cameras W3A.5 Douglas Chai and Florian Hock Visual Information Processing Research Group School of Engineering and Mathematics Edith
More informationOpenSet Face Recognitionbased Visitor Interface System
OpenSet Face Recognitionbased Visitor Interface System Hazım K. Ekenel, Lorant SzaszToth, and Rainer Stiefelhagen Computer Science Department, Universität Karlsruhe (TH) Am Fasanengarten 5, Karlsruhe
More informationClassifiers & Classification
Classifiers & Classification Forsyth & Ponce Computer Vision A Modern Approach chapter 22 Pattern Classification Duda, Hart and Stork School of Computer Science & Statistics Trinity College Dublin Dublin
More informationIntroduction to Pattern Recognition
Introduction to Pattern Recognition Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr CS 551, Spring 2009 CS 551, Spring 2009 c 2009, Selim Aksoy (Bilkent University)
More informationThe Scientific Data Mining Process
Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In
More informationJPEG compression of monochrome 2Dbarcode images using DCT coefficient distributions
Edith Cowan University Research Online ECU Publications Pre. JPEG compression of monochrome Dbarcode images using DCT coefficient distributions Keng Teong Tan Hong Kong Baptist University Douglas Chai
More informationRobert Collins CSE598G. More on Meanshift. R.Collins, CSE, PSU CSE598G Spring 2006
More on Meanshift R.Collins, CSE, PSU Spring 2006 Recall: Kernel Density Estimation Given a set of data samples x i ; i=1...n Convolve with a kernel function H to generate a smooth function f(x) Equivalent
More informationData Clustering. Dec 2nd, 2013 Kyrylo Bessonov
Data Clustering Dec 2nd, 2013 Kyrylo Bessonov Talk outline Introduction to clustering Types of clustering Supervised Unsupervised Similarity measures Main clustering algorithms kmeans Hierarchical Main
More informationLinear Threshold Units
Linear Threshold Units w x hx (... w n x n w We assume that each feature x j and each weight w j is a real number (we will relax this later) We will study three different algorithms for learning linear
More informationAssessment. Presenter: Yupu Zhang, Guoliang Jin, Tuo Wang Computer Vision 2008 Fall
Automatic Photo Quality Assessment Presenter: Yupu Zhang, Guoliang Jin, Tuo Wang Computer Vision 2008 Fall Estimating i the photorealism of images: Distinguishing i i paintings from photographs h Florin
More informationPalmprint Recognition with PCA and ICA
Abstract Palmprint Recognition with PCA and ICA Tee Connie, Andrew Teoh, Michael Goh, David Ngo Faculty of Information Sciences and Technology, Multimedia University, Melaka, Malaysia tee.connie@mmu.edu.my
More informationKeywords image processing, signature verification, false acceptance rate, false rejection rate, forgeries, feature vectors, support vector machines.
International Journal of Computer Application and Engineering Technology Volume 3Issue2, Apr 2014.Pp. 188192 www.ijcaet.net OFFLINE SIGNATURE VERIFICATION SYSTEM A REVIEW Pooja Department of Computer
More informationLecture 20: Clustering
Lecture 20: Clustering Wrapup of neural nets (from last lecture Introduction to unsupervised learning Kmeans clustering COMP424, Lecture 20  April 3, 2013 1 Unsupervised learning In supervised learning,
More informationColour Image Segmentation Technique for Screen Printing
60 R.U. Hewage and D.U.J. Sonnadara Department of Physics, University of Colombo, Sri Lanka ABSTRACT Screenprinting is an industry with a large number of applications ranging from printing mobile phone
More informationExample: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.
Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation:  Feature vector X,  qualitative response Y, taking values in C
More informationUsing Lexical Similarity in Handwritten Word Recognition
Using Lexical Similarity in Handwritten Word Recognition Jaehwa Park and Venu Govindaraju Center of Excellence for Document Analysis and Recognition (CEDAR) Department of Computer Science and Engineering
More informationTemplatebased Eye and Mouth Detection for 3D Video Conferencing
Templatebased Eye and Mouth Detection for 3D Video Conferencing Jürgen Rurainsky and Peter Eisert Fraunhofer Institute for Telecommunications  HeinrichHertzInstitute, Image Processing Department, Einsteinufer
More informationENVI Classic Tutorial: Classification Methods
ENVI Classic Tutorial: Classification Methods Classification Methods 2 Files Used in this Tutorial 2 Examining a Landsat TM Color Image 3 Reviewing Image Colors 3 Using the Cursor Location/Value 4 Examining
More informationBlood Vessel Classification into Arteries and Veins in Retinal Images
Blood Vessel Classification into Arteries and Veins in Retinal Images Claudia Kondermann and Daniel Kondermann a and Michelle Yan b a Interdisciplinary Center for Scientific Computing (IWR), University
More informationby the matrix A results in a vector which is a reflection of the given
Eigenvalues & Eigenvectors Example Suppose Then So, geometrically, multiplying a vector in by the matrix A results in a vector which is a reflection of the given vector about the yaxis We observe that
More informationECE 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. Preprocessing 8. 5. Feature Extraction 9. 6. Verification
More informationAPPM4720/5720: Fast algorithms for big data. Gunnar Martinsson The University of Colorado at Boulder
APPM4720/5720: Fast algorithms for big data Gunnar Martinsson The University of Colorado at Boulder Course objectives: The purpose of this course is to teach efficient algorithms for processing very large
More informationFacebook Friend Suggestion Eytan Daniyalzade and Tim Lipus
Facebook Friend Suggestion Eytan Daniyalzade and Tim Lipus 1. Introduction Facebook is a social networking website with an open platform that enables developers to extract and utilize user information
More informationBasics of microarrays. Petter Mostad 2003
Basics of microarrays Petter Mostad 2003 Why microarrays? Microarrays work by hybridizing strands of DNA in a sample against complementary DNA in spots on a chip. Expression analysis measure relative amounts
More informationCOLORBASED PRINTED CIRCUIT BOARD SOLDER SEGMENTATION
COLORBASED PRINTED CIRCUIT BOARD SOLDER SEGMENTATION TzSheng Peng ( 彭 志 昇 ), ChiouShann Fuh ( 傅 楸 善 ) Dept. of Computer Science and Information Engineering, National Taiwan University Email: r96922118@csie.ntu.edu.tw
More informationAnalysis of kiva.com Microlending Service! Hoda Eydgahi Julia Ma Andy Bardagjy December 9, 2010 MAS.622j
Analysis of kiva.com Microlending Service! Hoda Eydgahi Julia Ma Andy Bardagjy December 9, 2010 MAS.622j What is Kiva? An organization that allows people to lend small amounts of money via the Internet
More informationSYMMETRIC 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 informationCalculation of Minimum Distances. Minimum Distance to Means. Σi i = 1
Minimum Distance to Means Similar to Parallelepiped classifier, but instead of bounding areas, the user supplies spectral class means in ndimensional space and the algorithm calculates the distance between
More informationChapter 6. Orthogonality
6.3 Orthogonal Matrices 1 Chapter 6. Orthogonality 6.3 Orthogonal Matrices Definition 6.4. An n n matrix A is orthogonal if A T A = I. Note. We will see that the columns of an orthogonal matrix must be
More informationCS 591.03 Introduction to Data Mining Instructor: Abdullah Mueen
CS 591.03 Introduction to Data Mining Instructor: Abdullah Mueen LECTURE 3: DATA TRANSFORMATION AND DIMENSIONALITY REDUCTION Chapter 3: Data Preprocessing Data Preprocessing: An Overview Data Quality Major
More informationHANDSFREE PC CONTROL CONTROLLING OF MOUSE CURSOR USING EYE MOVEMENT
International Journal of Scientific and Research Publications, Volume 2, Issue 4, April 2012 1 HANDSFREE PC CONTROL CONTROLLING OF MOUSE CURSOR USING EYE MOVEMENT Akhil Gupta, Akash Rathi, Dr. Y. Radhika
More information3 An Illustrative Example
Objectives An Illustrative Example Objectives  Theory and Examples 2 Problem Statement 2 Perceptron  TwoInput Case 4 Pattern Recognition Example 5 Hamming Network 8 Feedforward Layer 8 Recurrent
More informationUsing Data Mining for Mobile Communication Clustering and Characterization
Using Data Mining for Mobile Communication Clustering and Characterization A. Bascacov *, C. Cernazanu ** and M. Marcu ** * Lasting Software, Timisoara, Romania ** Politehnica University of Timisoara/Computer
More informationDocument Image Retrieval using Signatures as Queries
Document Image Retrieval using Signatures as Queries Sargur N. Srihari, Shravya Shetty, Siyuan Chen, Harish Srinivasan, Chen Huang CEDAR, University at Buffalo(SUNY) Amherst, New York 14228 Gady Agam and
More informationColor Histogram Normalization using Matlab and Applications in CBIR. László Csink, Szabolcs Sergyán Budapest Tech SSIP 05, Szeged
Color Histogram Normalization using Matlab and Applications in CBIR László Csink, Szabolcs Sergyán Budapest Tech SSIP 05, Szeged Outline Introduction Demonstration of the algorithm Mathematical background
More informationAnalecta Vol. 8, No. 2 ISSN 20647964
EXPERIMENTAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN ENGINEERING PROCESSING SYSTEM S. Dadvandipour Institute of Information Engineering, University of Miskolc, Egyetemváros, 3515, Miskolc, Hungary,
More informationLeastSquares Intersection of Lines
LeastSquares Intersection of Lines Johannes Traa  UIUC 2013 This writeup derives the leastsquares solution for the intersection of lines. In the general case, a set of lines will not intersect at a
More informationImage Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode Value
IJSTE  International Journal of Science Technology & Engineering Volume 1 Issue 10 April 2015 ISSN (online): 2349784X Image Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode
More informationFace 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 informationMachine Learning using MapReduce
Machine Learning using MapReduce What is Machine Learning Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous
More informationIndex 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 informationInformation Fusion in LowResolution Iris Videos using Principal Components Transform
Information Fusion in LowResolution Iris Videos using Principal Components Transform Raghavender Jillela, Arun Ross West Virginia University {Raghavender.Jillela, Arun.Ross}@mail.wvu.edu Patrick J. Flynn
More informationNonlinear Iterative Partial Least Squares Method
Numerical Methods for Determining Principal Component Analysis Abstract Factors Béchu, S., RichardPlouet, M., Fernandez, V., Walton, J., and Fairley, N. (2016) Developments in numerical treatments for
More informationImage ContentBased Email Spam Image Filtering
Image ContentBased Email Spam Image Filtering Jianyi Wang and Kazuki Katagishi Abstract With the population of Internet around the world, email has become one of the main methods of communication among
More information15.062 Data Mining: Algorithms and Applications Matrix Math Review
.6 Data Mining: Algorithms and Applications Matrix Math Review The purpose of this document is to give a brief review of selected linear algebra concepts that will be useful for the course and to develop
More informationAn Enhanced Countermeasure Technique for Deceptive Phishing Attack
An Enhanced Countermeasure Technique for Deceptive Phishing Attack K. Selvan 1, Dr. M. Vanitha 2 Research Scholar and Assistant Professor, Department of Computer Science, JJ College of Arts and Science
More informationGreen = 0,255,0 (Target Color for E.L. Gray Construction) CIELAB RGB Simulation Result for E.L. Gray Match (43,215,35) Equal Luminance Gray for Green
Red = 255,0,0 (Target Color for E.L. Gray Construction) CIELAB RGB Simulation Result for E.L. Gray Match (184,27,26) Equal Luminance Gray for Red = 255,0,0 (147,147,147) Mean of Observer Matches to Red=255
More informationIEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 7, JULY 2009 1181
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 7, JULY 2009 1181 The Global Kernel kmeans Algorithm for Clustering in Feature Space Grigorios F. Tzortzis and Aristidis C. Likas, Senior Member, IEEE
More informationSYSTEMS OF EQUATIONS
SYSTEMS OF EQUATIONS 1. Examples of systems of equations Here are some examples of systems of equations. Each system has a number of equations and a number (not necessarily the same) of variables for which
More informationEfficient online Signature Verification System
International Journal of Engineering & Technology IJETIJENS Vol:10 No:04 42 Efficient online Signature Verification System Dr. S.A Daramola 1 and Prof. T.S Ibiyemi 2 1 Department of Electrical and Information
More informationA new Method for Face Recognition Using Variance Estimation and Feature Extraction
A new Method for Face Recognition Using Variance Estimation and Feature Extraction Walaa Mohamed 1, Mohamed Heshmat 2, Moheb Girgis 3 and Seham Elaw 4 1, 2, 4 Faculty of science, Mathematical and Computer
More informationILLUMINATION NORMALIZATION BASED ON SIMPLIFIED LOCAL BINARY PATTERNS FOR A FACE VERIFICATION SYSTEM. Qian Tao, Raymond Veldhuis
ILLUMINATION NORMALIZATION BASED ON SIMPLIFIED LOCAL BINARY PATTERNS FOR A FACE VERIFICATION SYSTEM Qian Tao, Raymond Veldhuis Signals and Systems Group, Faculty of EEMCS University of Twente, the Netherlands
More informationPrecision edge detection with bayer pattern sensors
Precision edge detection with bayer pattern sensors Prof.Dr.Ing.habil. Gerhard Linß Dr.Ing. Peter Brückner Dr.Ing. Martin Correns Folie 1 Agenda 1. Introduction 2. State of the art 3. Key aspects 1.
More informationClass #6: Nonlinear classification. ML4Bio 2012 February 17 th, 2012 Quaid Morris
Class #6: Nonlinear classification ML4Bio 2012 February 17 th, 2012 Quaid Morris 1 Module #: Title of Module 2 Review Overview Linear separability Nonlinear classification Linear Support Vector Machines
More informationComparison of Nonlinear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data
CMPE 59H Comparison of Nonlinear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data Term Project Report Fatma Güney, Kübra Kalkan 1/15/2013 Keywords: Nonlinear
More informationModelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches
Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches PhD Thesis by Payam Birjandi Director: Prof. Mihai Datcu Problematic
More informationAN IMPROVED DOUBLE CODING LOCAL BINARY PATTERN ALGORITHM FOR FACE RECOGNITION
AN IMPROVED DOUBLE CODING LOCAL BINARY PATTERN ALGORITHM FOR FACE RECOGNITION Saurabh Asija 1, Rakesh Singh 2 1 Research Scholar (Computer Engineering Department), Punjabi University, Patiala. 2 Asst.
More informationSignature Region of Interest using Auto cropping
ISSN (Online): 16940784 ISSN (Print): 16940814 1 Signature Region of Interest using Auto cropping Bassam AlMahadeen 1, Mokhled S. AlTarawneh 2 and Islam H. AlTarawneh 2 1 Math. And Computer Department,
More informationELECE8104 Stochastics models and estimation, Lecture 3b: Linear Estimation in Static Systems
Stochastics models and estimation, Lecture 3b: Linear Estimation in Static Systems Minimum Mean Square Error (MMSE) MMSE estimation of Gaussian random vectors Linear MMSE estimator for arbitrarily distributed
More informationSolving Systems of Linear Equations; Row Reduction
Harvey Mudd College Math Tutorial: Solving Systems of Linear Equations; Row Reduction Systems of linear equations arise in all sorts of applications in many different fields of study The method reviewed
More information