CHAPTER 5 SENDER AUTHENTICATION USING FACE BIOMETRICS

Size: px
Start display at page:

Download "CHAPTER 5 SENDER AUTHENTICATION USING FACE BIOMETRICS"

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 biometric-based 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 (http://nn.cs.utexas.edu/web-pubs/htmlbook96/wiskott/). 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 PRE-PROCESSING 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 non-skin regions. After that, the human face within the skin regions is located and cropped. In order to segment the human skin regions from the non-skin regions based on color, a reliable skin color model of different people is needed (http://www-cs-students.stanford.edu/ ~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 low-pass 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 Skin-color of Different People The color histogram revealed that the distributions of the skin-color 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 (x-m) T C -1 (x-m)] (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 skin-likelihood image will be a gray-scale 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 skin-colored regions are effectively segmented from the non-skin 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 skin-likelihood 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 (Zhi-li and Chun-hung 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 user-1 of group A sends data to his leader, the facial image of user-1 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 pre-processing 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 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 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

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

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

PCA to Eigenfaces. CS 510 Lecture #16 March 23 th A 9 dimensional PCA example

PCA 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 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

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

BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES

BEHAVIOR 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 information

SIGNATURE VERIFICATION

SIGNATURE 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 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

Authentication Scheme for ATM Based On Biometric K. Kavitha, II-MCA IFET COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS

Authentication Scheme for ATM Based On Biometric K. Kavitha, II-MCA IFET COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS Authentication Scheme for ATM Based On Biometric K. Kavitha, II-MCA IFET COLLEGE OF ENGINEERING DEPARTMENT OF COMPUTER APPLICATIONS ABSTRACT: Biometrics based authentication is a potential candidate to

More information

Volume 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 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 information

Lecture 9: Introduction to Pattern Analysis

Lecture 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 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

EM Clustering Approach for Multi-Dimensional Analysis of Big Data Set

EM Clustering Approach for Multi-Dimensional Analysis of Big Data Set EM Clustering Approach for Multi-Dimensional Analysis of Big Data Set Amhmed A. Bhih School of Electrical and Electronic Engineering Princy Johnson School of Electrical and Electronic Engineering Martin

More information

Color to Grayscale Conversion with Chrominance Contrast

Color 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 information

Review Jeopardy. Blue vs. Orange. Review Jeopardy

Review Jeopardy. Blue vs. Orange. Review Jeopardy Review Jeopardy Blue vs. Orange Review Jeopardy Jeopardy Round Lectures 0-3 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 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

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

Canny Edge Detection

Canny 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 information

Environmental Remote Sensing GEOG 2021

Environmental 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 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

Principal components analysis

Principal 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 k-dimension subspace, where k

More information

2.11 CMC curves showing the performance of sketch to digital face image matching. algorithms on the CUHK database... 40

2.11 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 information

Signature verification using Kolmogorov-Smirnov. statistic

Signature verification using Kolmogorov-Smirnov. statistic Signature verification using Kolmogorov-Smirnov 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 information

Component Ordering in Independent Component Analysis Based on Data Power

Component 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 information

Blind 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 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 information

Mugshot Identification from Manipulated Facial Images Chennamma H.R.* and Lalitha Rangarajan

Mugshot 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 information

DESIGN OF DIGITAL SIGNATURE VERIFICATION ALGORITHM USING RELATIVE SLOPE METHOD

DESIGN 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 information

DEVELOPING AN IMAGE RECOGNITION ALGORITHM FOR FACIAL AND DIGIT IDENTIFICATION

DEVELOPING 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 information

Palmprint as a Biometric Identifier

Palmprint 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 information

A Matlab Project in Optical Character Recognition (OCR)

A 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 information

PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION

PATTERN 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 information

A New Robust Algorithm for Video Text Extraction

A 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 information

3)Skilled Forgery: It is represented by suitable imitation of genuine signature mode.it is also called Well-Versed Forgery[4].

3)Skilled Forgery: It is represented by suitable imitation of genuine signature mode.it is also called Well-Versed 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 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

Machine Learning for Data Science (CS4786) Lecture 1

Machine Learning for Data Science (CS4786) Lecture 1 Machine Learning for Data Science (CS4786) Lecture 1 Tu-Th 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 information

Lecture 4: Thresholding

Lecture 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 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

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

Locating and Decoding EAN-13 Barcodes from Images Captured by Digital Cameras

Locating and Decoding EAN-13 Barcodes from Images Captured by Digital Cameras Locating and Decoding EAN-13 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 information

Open-Set Face Recognition-based Visitor Interface System

Open-Set Face Recognition-based Visitor Interface System Open-Set Face Recognition-based Visitor Interface System Hazım K. Ekenel, Lorant Szasz-Toth, and Rainer Stiefelhagen Computer Science Department, Universität Karlsruhe (TH) Am Fasanengarten 5, Karlsruhe

More information

Classifiers & Classification

Classifiers & 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 information

Introduction to Pattern Recognition

Introduction 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 information

The Scientific Data Mining Process

The 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 information

JPEG compression of monochrome 2D-barcode images using DCT coefficient distributions

JPEG compression of monochrome 2D-barcode images using DCT coefficient distributions Edith Cowan University Research Online ECU Publications Pre. JPEG compression of monochrome D-barcode images using DCT coefficient distributions Keng Teong Tan Hong Kong Baptist University Douglas Chai

More information

Robert Collins CSE598G. More on Mean-shift. R.Collins, CSE, PSU CSE598G Spring 2006

Robert Collins CSE598G. More on Mean-shift. R.Collins, CSE, PSU CSE598G Spring 2006 More on Mean-shift 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 information

Data Clustering. Dec 2nd, 2013 Kyrylo Bessonov

Data 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 k-means Hierarchical Main

More information

Linear Threshold Units

Linear 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 information

Assessment. Presenter: Yupu Zhang, Guoliang Jin, Tuo Wang Computer Vision 2008 Fall

Assessment. 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 information

Palmprint Recognition with PCA and ICA

Palmprint 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 information

Keywords image processing, signature verification, false acceptance rate, false rejection rate, forgeries, feature vectors, support vector machines.

Keywords 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 3-Issue2, Apr 2014.Pp. 188-192 www.ijcaet.net OFFLINE SIGNATURE VERIFICATION SYSTEM -A REVIEW Pooja Department of Computer

More information

Lecture 20: Clustering

Lecture 20: Clustering Lecture 20: Clustering Wrap-up of neural nets (from last lecture Introduction to unsupervised learning K-means clustering COMP-424, Lecture 20 - April 3, 2013 1 Unsupervised learning In supervised learning,

More information

Colour Image Segmentation Technique for Screen Printing

Colour Image Segmentation Technique for Screen Printing 60 R.U. Hewage and D.U.J. Sonnadara Department of Physics, University of Colombo, Sri Lanka ABSTRACT Screen-printing is an industry with a large number of applications ranging from printing mobile phone

More information

Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.

Example: 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 information

Using Lexical Similarity in Handwritten Word Recognition

Using 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 information

Template-based Eye and Mouth Detection for 3D Video Conferencing

Template-based Eye and Mouth Detection for 3D Video Conferencing Template-based Eye and Mouth Detection for 3D Video Conferencing Jürgen Rurainsky and Peter Eisert Fraunhofer Institute for Telecommunications - Heinrich-Hertz-Institute, Image Processing Department, Einsteinufer

More information

ENVI Classic Tutorial: Classification Methods

ENVI 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 information

Blood Vessel Classification into Arteries and Veins in Retinal Images

Blood 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 information

by the matrix A results in a vector which is a reflection of the given

by 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 y-axis We observe that

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

APPM4720/5720: 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 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 information

Facebook Friend Suggestion Eytan Daniyalzade and Tim Lipus

Facebook 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 information

Basics of microarrays. Petter Mostad 2003

Basics 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 information

COLOR-BASED PRINTED CIRCUIT BOARD SOLDER SEGMENTATION

COLOR-BASED PRINTED CIRCUIT BOARD SOLDER SEGMENTATION COLOR-BASED PRINTED CIRCUIT BOARD SOLDER SEGMENTATION Tz-Sheng Peng ( 彭 志 昇 ), Chiou-Shann Fuh ( 傅 楸 善 ) Dept. of Computer Science and Information Engineering, National Taiwan University E-mail: r96922118@csie.ntu.edu.tw

More information

Analysis 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 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 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

Calculation of Minimum Distances. Minimum Distance to Means. Σi i = 1

Calculation 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 n-dimensional space and the algorithm calculates the distance between

More information

Chapter 6. Orthogonality

Chapter 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 information

CS 591.03 Introduction to Data Mining Instructor: Abdullah Mueen

CS 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 information

HANDS-FREE PC CONTROL CONTROLLING OF MOUSE CURSOR USING EYE MOVEMENT

HANDS-FREE PC CONTROL CONTROLLING OF MOUSE CURSOR USING EYE MOVEMENT International Journal of Scientific and Research Publications, Volume 2, Issue 4, April 2012 1 HANDS-FREE PC CONTROL CONTROLLING OF MOUSE CURSOR USING EYE MOVEMENT Akhil Gupta, Akash Rathi, Dr. Y. Radhika

More information

3 An Illustrative Example

3 An Illustrative Example Objectives An Illustrative Example Objectives - Theory and Examples -2 Problem Statement -2 Perceptron - Two-Input Case -4 Pattern Recognition Example -5 Hamming Network -8 Feedforward Layer -8 Recurrent

More information

Using Data Mining for Mobile Communication Clustering and Characterization

Using 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 information

Document Image Retrieval using Signatures as Queries

Document 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 information

Color 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 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 information

Analecta Vol. 8, No. 2 ISSN 2064-7964

Analecta Vol. 8, No. 2 ISSN 2064-7964 EXPERIMENTAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN ENGINEERING PROCESSING SYSTEM S. Dadvandipour Institute of Information Engineering, University of Miskolc, Egyetemváros, 3515, Miskolc, Hungary,

More information

Least-Squares Intersection of Lines

Least-Squares Intersection of Lines Least-Squares Intersection of Lines Johannes Traa - UIUC 2013 This write-up derives the least-squares solution for the intersection of lines. In the general case, a set of lines will not intersect at a

More information

Image Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode Value

Image 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): 2349-784X Image Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode

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

Machine Learning using MapReduce

Machine 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 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

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

Nonlinear Iterative Partial Least Squares Method

Nonlinear Iterative Partial Least Squares Method Numerical Methods for Determining Principal Component Analysis Abstract Factors Béchu, S., Richard-Plouet, M., Fernandez, V., Walton, J., and Fairley, N. (2016) Developments in numerical treatments for

More information

Image Content-Based Email Spam Image Filtering

Image Content-Based Email Spam Image Filtering Image Content-Based 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 information

15.062 Data Mining: Algorithms and Applications Matrix Math Review

15.062 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 information

An Enhanced Countermeasure Technique for Deceptive Phishing Attack

An 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 information

Green = 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

Green = 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 information

IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 7, JULY 2009 1181

IEEE 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 k-means Algorithm for Clustering in Feature Space Grigorios F. Tzortzis and Aristidis C. Likas, Senior Member, IEEE

More information

SYSTEMS OF EQUATIONS

SYSTEMS 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 information

Efficient on-line Signature Verification System

Efficient on-line Signature Verification System International Journal of Engineering & Technology IJET-IJENS Vol:10 No:04 42 Efficient on-line Signature Verification System Dr. S.A Daramola 1 and Prof. T.S Ibiyemi 2 1 Department of Electrical and Information

More information

A new Method for Face Recognition Using Variance Estimation and Feature Extraction

A 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 information

ILLUMINATION 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 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 information

Precision edge detection with bayer pattern sensors

Precision 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 information

Class #6: Non-linear classification. ML4Bio 2012 February 17 th, 2012 Quaid Morris

Class #6: Non-linear classification. ML4Bio 2012 February 17 th, 2012 Quaid Morris Class #6: Non-linear classification ML4Bio 2012 February 17 th, 2012 Quaid Morris 1 Module #: Title of Module 2 Review Overview Linear separability Non-linear classification Linear Support Vector Machines

More information

Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data

Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data CMPE 59H Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data Term Project Report Fatma Güney, Kübra Kalkan 1/15/2013 Keywords: Non-linear

More information

Modelling, 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 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 information

AN IMPROVED DOUBLE CODING LOCAL BINARY PATTERN ALGORITHM FOR FACE RECOGNITION

AN 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 information

Signature Region of Interest using Auto cropping

Signature Region of Interest using Auto cropping ISSN (Online): 1694-0784 ISSN (Print): 1694-0814 1 Signature Region of Interest using Auto cropping Bassam Al-Mahadeen 1, Mokhled S. AlTarawneh 2 and Islam H. AlTarawneh 2 1 Math. And Computer Department,

More information

ELEC-E8104 Stochastics models and estimation, Lecture 3b: Linear Estimation in Static Systems

ELEC-E8104 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 information

Solving Systems of Linear Equations; Row Reduction

Solving 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