Topics in CSE:



Section Id: 570220,

Tuesday, Thursday, 3:30pm - 4:50pm

HSS 1138









10/2/06: Project description posted

10/3/06: Confirmed that E-reserves are available at http://reserves.ucsd.edu/eres/coursepage.aspx?cid=9146&page=docs

10/12/06: Homework 1 posted

10/24/06: Homework 2 and Homework 3 posted

10/31/06: Homework 2 has been updated!



Instructor: David Kriegman

Office: EBU3b, Room 4120

Phone: (858) 822-2424

Email: kriegman at cs.ucsd.edu

Office Hour: Wednesday 1:30-2:30


Class Description: Biometrics is the science of determining a person's identity by measuring his/her physiological characteristics. Fingerprinting, most widely known for its role in forensics, was used to sign and validate contracts in the 7th century during China's Tang Dynasty; today, laptop computers use automatic fingerprint recognition instead of passwords. Technologies are being developed to verify or identify individuals based on measurements of the face, hand geometry, iris, retina, finger, ear, voice, speech, signature, lip motion, skin reflectance, DNA, and even body odor. In this course we will explore the advances in biometrics as well as the machine learning techniques behind them.


Required Text: none

See E-reserve at: http://reserves.ucsd.edu/eres/coursepage.aspx?cid=9146&page=docs


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


Assignments: 40%

Quiz: 10%

Final Project: 50%

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.




Homework 0: Getting Started with Matlab, Due Thursday, Sept. 28, 2006). Follow the assignment from CSE152 last spring as described in the following PDF file with Assignment 0.  A test image for the assignment is here. You only need to hand it the hardcopy, not the electronic part. If you have already done the tutorial as part of another class, you do not need to repeat Part I, but you need to do Part 2. If you took CSE152 last year, you can hand in your result for part 2 from last quarter.


PROJECT: Here is the project description including due dates.

Homework 1: Pattern Classification, due 10/19/06

Homework 2: More Pattern Classification, due 10/31/06

Homework 3: Hand geometry-based recognition, due 11/7/06. Data files

Homework 4: Finding the Candy Man using fingerprints, due 12/1/06, Data files.




[ Note that this Syllabus is tentative & subject to change]



To lecture





Linear algebra review

Random variables review

Intro to Biometrics

A.K. Jain, A. Ross and S. Prabhakar, "An Introduction to Biometric Recognition", BIOMETRICS: Personal Identification in Networked Society, Springer, 1998


Sep. 26

Statistical Pattern Recognition, Bayesian Decision Theory

Duda & Hart, Chapter 1, 2.1-2.4


Sep. 28

Bayesian Decision Theory, Gaussian distributions

Duda & Hart, Sec. 2.5-2.8


Oct. 3

Image Formation


Oct. 5

Parameter Estimation,

Duda & Hart, Sec. 3.1-3.5, 3.7


Oct. 10

Parameter Estimation, cont


Oct. 12

Non-parametric classification

Duda & Hart, Sec. 4.1-4.5


Oct. 17

Nearest Neighbor density estimation and classification


Oct. 19

Linear Discriminant Functions


Oct. 24

Support Vector Machines

1. Duda, Hart & Stork, Sec. 5.11

2. "A Tutorial on Support Vector Machines for Pattern Recognition," Christopher J.C. Burges


Oct. 26

Hand Geometry and Recognition

R. Zunkel: Hand Geometry based Verification, BIOMETRICS: Personal Identification in Networked Society, Springer, 1998


Oct. 31

Face Recognition I


Nov. 2

Fingerprint Recognition I – Guest lecture by Serge Belongie


Nov. 7

Fingerprint Recognition II

Minutiae extraction


Nov. 9

Fingerprint Recognition III

RANSAC Notes from Hartley & Zisserman


Nov. 14

Face Recognition II


Nov. 16

Face Recognition III


Nov. 21

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



Nov. 28

Biometrics Policy


Nov. 30

Project presentations



Notes and links


Programming languages: 

The primary language will be Matlab. . Click here for Serge Belongie’s Matlab resource links.


A good book on Computer Vision

Introductory Techniques for 3-D Computer Vision, Trucco and Verri (textbook for CSE152)


Some 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.



Some useful links: