CSE190-C00
Topics in CSE:
BIOMETRICS
Winter 2012
Tuesday, Thursday, 11:00-12:20
Center Hall 224c
http://www.cs.ucsd.edu/classes/wi12/cse190-c/
Instructor: David Kriegman
Office: EBU3b, Room 4120
Phone: (858) 822-2424
Email: kriegman
at cs.ucsd.edu
Office Hour: Tuesday 1:30-2:30
Class Description: From
Minority Report, Angels & Demons, Mission Impossible, Blade Runner, Gattaca, and The Bourne Identity to The Simpsons and The Incredibles, biometric technologies like face recognition,
fingerprint recognition, iris scans, and voice recognition are either a nearly
impenetrable barriers to be cleverly foiled or the butt of a joke. Yet, biometrics are used every day to
auto-tag photos on Facebook and iPhoto
as well as in hardcore applications in immigration control and counter
terrorism.
Biometrics are used to identify
individuals from measurements of the face, hand geometry, iris, retina, finger,
ear, voice, speech, signature, lip motion, skin reflectance, DNA, and even body
odor. The first half of the course
provides the background in machine learning and imaging for anyone who has taken
calculus, linear algebra and probability and statistics. The second half of course will detail
individual biometrics, methods for spoofing biometrics, and policy
implications.
In a project spaced over the quarter,
students will implement a biometric method, system, or application of their
choice and appropriate to their background. Past topics have included ace recognition using Xbox Kinect, identification from electrocardiograms, face
recognition for Chez Bob, gesture interfaces, and recognition from wet
fingerprints.
Texts:
Introduction
to Biometrics, A. Jain, A. Ross, K. Nandakumar,
Springer, 2012
See
E-reserve at: TBD
Prerequisites: Linear
algebra and Multivariable calculus (e.g. Math 20A & 20F), probability and
statistics (e.g., Math 183 or CSE190, A Practical Introduction to Probability and
Statistics), a good working knowledge of C, C++ or Matlab
programming.
Programming:
Assignments will include both written problem sets and programming assignments
in Matlab. Students can either purchase the Matlab student edition or use copies available on
University machines such as are available in the APE Lab
Grading:
Assignments:
40%
Term Project: 60%
Late Policy: Written
homework will be due in class and accepted thereafter with a penalty of 10% per
day starting from the due date. Programming assignments will have a hand-in
procedure described with the assignment, and also has a 10% per day late
penalty. No assignments will be accepted after the graded assignments have been
returned or the solutions have been released.
Assignments:
Homework 0: Getting
Started with Matlab, Due Thursday, Jan. 19, 2012).
Follow the assignment from CSE152 as described in the following PDF file with
Assignment 0. A test image for the assignment is here.
You only need to hand in the hardcopy, not the electronic part.
Homework 1: Posted and due in class on 1/24/12.
Project: The description of
the project is here, and the first deadline is
1/26/12.
Homework 2: Posted and due in class
2/14/2012
Homework 3: Hand
geometry-based recognition, due March 6, 2012. Data files: part1, part 2 (jpgs, pdfs)
Homework4: Finding
the CandyMan: Fingerprint recognition, due 3/23/212,. Images: fingerprint-images-HW.zip
Syllabus
[ Note that
this Syllabus is tentative & subject to change]
|
Week |
Date/Link To lecture notes |
Topic/Readings |
|
Biometric
Recognition, A.K. Jain, Nature, September 2007 |
||
|
|
Statistical Pattern Recognition,
Bayesian Decision Theory Duda & Hart, Chapter 1, 2.1-2.4 |
|
|
2 |
Bayesian Decision Theory, Gaussian
distributions Duda & Hart, Sec. 2.5-2.8 |
|
|
|
Normal distributions |
|
|
3 |
Image Formation |
|
|
|
Parameter Estimation, Duda & Hart, Sec. 3.1-3.5, 3.7 |
|
|
4 |
Non-parametric classification Duda & Hart, Sec. 4.1-4.5 |
|
|
|
Nearest Neighbor density estimation
and classification |
|
|
5 |
Linear Discriminant
Functions |
|
|
|
Support Vector Machines 1. Duda,
Hart & Stork, Sec. 5.11 2. "A
Tutorial on Support Vector Machines for Pattern Recognition," Christopher
J.C. Burges |
|
|
6 |
Face Recognition I |
|
|
|
Face Recognition II |
|
|
7 |
Face Recognition III -- Dimensionality reduction |
|
|
|
Hand Geometry and Recognition R. Zunkel: Hand Geometry
based Verification, BIOMETRICS: Personal Identification in Networked
Society, Springer, 1998 |
|
|
8 |
Face Recognition –
illumination, pose and attributes |
|
|
|
Face Recognition –
illumination, pose and attributes |
|
|
9 |
Fingerprint Recognition I Minutiae extraction |
|
|
|
Fingerprint Recognition II: Minutae Matching See Section 6.1.4 of Computer Vision: Algorithms and Applications,
Rick Szeliski, Draft |
|
|
10 |
Iris Recognition J. Daugman:
Recognizing
Persons by Their Iris Patterns, BIOMETRICS: Personal Identification in
Networked Society, Springer, 1998. J. Daugman
(1993) "High
confidence visual recognition of persons by a test of statistical
independence." IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 15(11), pp. 1148-1161 |
|
|
|
Mar. 15 |
Project presentations |
Notes and links
Programming
languages:
The
primary language will be Matlab. . Click here for Serge BelongieÕs Matlab resource links.
Computer Vision Books
1.
Introductory Techniques for 3-D
Computer Vision, Trucco and Verri
(textbook for CSE152)
2.
ÒComputer Vision: Algorithms and
ApplicationsÓ, Richard Szeliski, An online copyof the book is available at: http://szeliski.org/Book/.
General Biometrics Books
1. D. Maltoni,
D. Maio, A. K. Jain, and S. Prabhakar,
Handbook of Fingerprint Recognition, Springer Verlag,
2003.
2. K. Jain, R. Bolle,
S. Pankanti (Eds.), BIOMETRICS: Personal
Identification in Networked Society, Kluwer Academic
Publishers, 1999.
3. Ruud M. Bolle et al., Guide to Biometrics, Springer, 2004.
4. Anil K Jain, Patrick Flynn,; Arun A. Ross,
Handbook of Biometrics, Springer 2008.
Some useful links:
