Biometrics & Security Tutorial 2. 1. (a) What is “Pattern”? According to its definition in (P3:4-7), could you list some patterns in your daily life?

Slides:

Advertisements

Similar presentations

Biometrics and Security Tutorial 3. 1 (a) What is the scatter matrix (P4: 21)? Understand what about eigenvector and eigenvalue as well as their functions?

Advertisements

Face Recognition and Biometric Systems Eigenfaces (2)

Biometrics & Security Tutorial 9. 1 (a) What is palmprint and palmprint authentication? (P10: 9-10)

1er. Escuela Red ProTIC - Tandil, de Abril, 2006 Principal component analysis (PCA) is a technique that is useful for the compression and classification.

Biometrics & Security Tutorial 5. 1 (a) Understand two stages (Enrollment and Authentication) in a fingerprint system. (P6: 12)

Biometrics & Security Tutorial 7. 1 (a) Please compare two different kinds of biometrics technologies: Retina and Iris. (P8:2-3)

Region labelling Giving a region a name. Image Processing and Computer Vision: 62 Introduction Region detection isolated regions Region description properties.

Chapter 1: Introduction to Pattern Recognition

Lecture 20 Object recognition I

Instructor: Dr. G. Bebis Reza Amayeh Fall 2005

Aula 5 Alguns Exemplos PMR5406 Redes Neurais e Lógica Fuzzy.

RBF Neural Networks x x1 Examples inside circles 1 and 2 are of class +, examples outside both circles are of class – What NN does.

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

Biometrics & Security Tutorial (a) Why multi-biometrics? (P13: 3-6)

Chapter 2: Pattern Recognition

Pattern Recognition Topic 1: Principle Component Analysis Shapiro chap

Biometrics and Security Tutorial 8. 1 (a) Understand the enrollment and verification of hand geometry? (P9: 8)

Multispectral Remote Sensing Multispec program from PurdueMultispec On Information Extraction Principles for Hyperspectral Data, David Landgrebe, School.

Pattern Recognition. Introduction. Definitions.. Recognition process. Recognition process relates input signal to the stored concepts about the object.

Biometrics & Security Tutorial 6. 1 (a) Understand why use face (P7: 3-4) and face recognition system (P7: 5-10)

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

Radial-Basis Function Networks

EE513 Audio Signals and Systems Statistical Pattern Classification Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.

METU Informatics Institute Min720 Pattern Classification with Bio-Medical Applications Lecture Notes by Neşe Yalabık Spring 2011.

Multiclass object recognition

Case Studies Dr Lee Nung Kion Faculty of Cognitive Sciences and Human Development UNIVERSITI MALAYSIA SARAWAK.

Summarized by Soo-Jin Kim

嵌入式視覺 Pattern Recognition for Embedded Vision Template matching Statistical / Structural Pattern Recognition Neural networks.

1 Template-Based Classification Method for Chinese Character Recognition Presenter: Tienwei Tsai Department of Informaiton Management, Chihlee Institute.

Machine Vision for Robots

CPSC 601 Lecture Week 5 Hand Geometry. Outline: 1.Hand Geometry as Biometrics 2.Methods Used for Recognition 3.Illustrations and Examples 4.Some Useful.

Presented by Tienwei Tsai July, 2005

Kumar Srijan ( ) Syed Ahsan( ). Problem Statement To create a Neural Networks based multiclass object classifier which can do rotation,

By Doğaç Başaran & Erdem Yörük

Support Vector Machines Mei-Chen Yeh 04/20/2010. The Classification Problem Label instances, usually represented by feature vectors, into one of the predefined.

Compiled By: Raj G Tiwari.  A pattern is an object, process or event that can be given a name.  A pattern class (or category) is a set of patterns sharing.

3.1 Quadratic Functions. Polynomials- classified by degree (highest exponent) Degree: 0 -constant function-horizontal line 1 -linear function- 2 -quadratic.

November 13, 2014Computer Vision Lecture 17: Object Recognition I 1 Today we will move on to… Object Recognition.

1 Pattern Recognition Pattern recognition is: 1. A research area in which patterns in data are found, recognized, discovered, …whatever. 2. A catchall.

Eyes detection in compressed domain using classification Eng. Alexandru POPA Technical University of Cluj-Napoca Faculty.

Chapter 4: Pattern Recognition. Classification is a process that assigns a label to an object according to some representation of the object’s properties.

Machines that Make Decisions Instructor: Edmondo Trentin

Levels of Image Data Representation 4.2. Traditional Image Data Structures 4.3. Hierarchical Data Structures Chapter 4 – Data structures for.

November 30, PATTERN RECOGNITION. November 30, TEXTURE CLASSIFICATION PROJECT Characterize each texture so as to differentiate it from one.

Digital Image Processing & Pattern Analysis (CSCE 563) Introduction to Pattern Analysis Prof. Amr Goneid Department of Computer Science & Engineering The.

Chapter 12 Object Recognition Chapter 12 Object Recognition 12.1 Patterns and pattern classes Definition of a pattern class:a family of patterns that share.

Chapter 20 Classification and Estimation Classification – Feature selection Good feature have four characteristics: –Discrimination. Features.

Introduction to Pattern Recognition (การรู้จํารูปแบบเบื้องต้น)

1 Machine Vision. 2 VISION the most powerful sense.

Text From Corners: A Novel Approach to Detect Text and Caption in Videos Xu Zhao, Kai-Hsiang Lin, Yun Fu, Member, IEEE, Yuxiao Hu, Member, IEEE, Yuncai.

Line Matching Jonghee Park GIST CV-Lab..  Lines –Fundamental feature in many computer vision fields 3D reconstruction, SLAM, motion estimation –Useful.

Computer Vision Lecture 7 Classifiers. Computer Vision, Lecture 6 Oleh Tretiak © 2005Slide 1 This Lecture Bayesian decision theory (22.1, 22.2) –General.

Machine Learning for Computer Security

Hand Geometry Recognition

Introduction Machine Learning 14/02/2017.

IMAGE PROCESSING RECOGNITION AND CLASSIFICATION

Traffic Sign Recognition Using Discriminative Local Features Andrzej Ruta, Yongmin Li, Xiaohui Liu School of Information Systems, Computing and Mathematics.

Section 5.1 Modeling Data with Quadratic Functions Objective: Students will be able to identify quadratic functions and graphs, and to model data with.

Pattern Recognition Sergios Theodoridis Konstantinos Koutroumbas

Face Recognition and Detection Using Eigenfaces

Learning with information of features

Presented by :- Vishal Vijayshankar Mishra

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

Object Recognition Today we will move on to… April 12, 2018

Creating Data Representations

EE513 Audio Signals and Systems

Faculty of Science IT Department Lecturer: Raz Dara MA.

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

Pattern Recognition and Training

Pattern Recognition and Training

Presentation transcript:

Biometrics & Security Tutorial 2

1. (a) What is “Pattern”? According to its definition in (P3:4-7), could you list some patterns in your daily life?

1. (b) “Pattern Recognition (PR)” is defined in P3:8. Could you give some PR samples and tell which features you use?

A thin personA fat person Height: 180 cmHeight: 150 cm Weight: 50 kgWeight: 100 kg

A image of 100x250 pixels: the grayscale of each pixel is recorded as g(i,j) (0<g(i,j)<256)

1. (c) In P3:11-13, a PR system is given. Please understand each function in the system.

1. (d) Understand the definitions related to PR: Classification, Recognition, Description, Pattern Class and Preprocessing (P3:14-15). Why classification and recognition require the different classes, c and c+1, respectively.

1. (e) What is correlation between two vectors, x and y? (P3: 37) (Maximum when x and y point in the same direction). Please use this definition in the function ||x-mk|| (P3: 34). How about orthogonal or uncorrelated between two vectors?

The error between x and m k can be computed as || x-m k || The correlation between x and m k can be computed as x’∙m k /||x||∙||m k || (cosine)

2. A 3-D example for pattern extraction is defined in P3: There are four kinds of features are extracted. Please select these features according to some guidelines (P3: 27-28) to classify the given six objects. (One selection: Edge + Symmetry + Axis)

3. A minimum-error classifier based on || x - mk || is defined in P3: 34. How to get a linear discriminant function, g(x) = m' x || m || in P3: 39? Which difference between the following two classifiers using the approaches mentioned above.

linear discriminant function Minimum error classifier h k (x) = || x - m k || The minimum h k (x) means the best match Usually we would like use something whose maximum means best X is invariant to k The minimum of h k (x) need the maximum of We define

|| x – m 1 || = =

In P3: 33 there are two templates (“D” and “O”) and ten input samples, each five. Please classify the third input example in the “D” row by using the maximum correlation approach and minimum error approach, respectively. (For Maximum Correlation Approach: “D”-Input: 82/90=91% and “O”-Input: 66/90=73%)

(1)(3)(2) S 12 = 11 Correlation of (1) & (2) is 11/16 Error of (1) & (2) is 5/ S 13 = 10 Correlation of (1) & (3) is 10/16 Error of (1) & (3) is 6/