Topics in CSE:



Winter 2012

Tuesday, Thursday, 11:00-12:20

Center Hall 224c






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. 



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



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.



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





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



To lecture




Jan. 10

Biometric Recognition, A.K. Jain, Nature, September 2007 

Linear algebra review

Random variables review


Jan. 12

Statistical Pattern Recognition, Bayesian Decision Theory

Duda & Hart, Chapter 1, 2.1-2.4


Jan. 17

Bayesian Decision Theory, Gaussian distributions

Duda & Hart, Sec. 2.5-2.8


Jan. 19

Normal distributions


Jan. 24

Image Formation


Jan. 26

Parameter Estimation,

Duda & Hart, Sec. 3.1-3.5, 3.7


Jan. 31

Non-parametric classification

Duda & Hart, Sec. 4.1-4.5


Feb. 2

Nearest Neighbor density estimation and classification


Feb. 7

Linear Discriminant Functions


Feb. 9

More notes

Support Vector Machines

1. Duda, Hart & Stork, Sec. 5.11

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


Feb. 14

Face Recognition I


Feb. 16

Face Recognition II


Feb. 21

Face Recognition III  -- Dimensionality reduction


Feb. 23

Hand Geometry and Recognition

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


Feb. 28

Face Recognition – illumination, pose and attributes


Mar. 1

Face Recognition – illumination, pose and attributes


Mar. 6

Fingerprint Recognition I

Minutiae extraction


Mar. 8

Fingerprint Recognition II: Minutae Matching

See Section 6.1.4 of  Computer Vision: Algorithms and Applications, Rick Szeliski, Draft


Mar. 13

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 Belongie’s Matlab resource links.


Computer Vision Books

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

2.     Computer Vision: Algorithms and Applications”, 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: