This course will cover the fundamental concepts and design implications required to implement biometric systems. Key approaches and machine learning techniques specific to vision based, speech based, and behavioral based biometric systems will be discussed. Biometric system performance evaluation and issues related to security and privacy will also be addressed. Topics include: the basic biometric approach, features and feature extraction, data set formats, supervised and unsupervised machine learning, dimensionality reduction and performance evaluation, image based biometric techniques, speech based biometric techniques, behavioral based biometric techniques, and biometric problems and ethical issues.
Prerequisites: Programming skills up to data structures and knowledge of statistics will be useful. No prior experience with biometrics or machine learning is required.
Biometrics: Personal Identification in Networked Society
Jain, Bolle, Pankanti
Data Mining: Practical Machine Learning Tools and Techniques
Ricardo A. Calix, Ph.D.
Computer Information Technology and Graphics
Purdue University Calumet
Tuesday and Thursday (2-4 PM)
Assignments: There will be 4 individual assignments with informal lab demonstrations plus one final project with a formal in-class presentation. Graduate students will have one additional writing assignment associated with the final project.
The grading of each assignment:
To be determined.
To be determined.
The live, in-lab demonstration and description of your completed project. Be ready for this.
Something extra. You are free to enhance your submission in any way you like. Your addition should be creative.
Example problems will be provided as required.
We will use the following software:
The following libraries may be of use.
|Jan 15||Jan 16||Jan 17
L1: The Biometric System Pipeline
Lab 1: Blood Vessel Recog. in Matlab
|Jan 20||Jan 21|
|Jan 22||Jan 23||Jan 24||Jan 25
||Jan 26||Jan 27||Jan 28|
|Jan 29||Jan 30
||Jan 31||Feb 1
||Feb 3||Feb 4|
|Feb 5||Feb 6||Feb 7
L4: Machine Learning 2
Lab 4: Image Analysis Basics with Matlab
|Feb 10||Feb 11|
|Feb 12||Feb 13||Feb 14
L5: Machine Learning 3
Lab 5: WEKA Experimenter and PCA
|Feb 17||Feb 18|
|Feb 19||Feb 20||Feb 21
L6: Image Segmentation
Lab 6: Image Segmentation with Matlab
|Feb 24||Feb 25|
|Feb 26||Feb 27||Feb28
L7: Face Recognition and EigenFaces
Lab 7: PCA and EigenFaces
|Mar 2||Mar 3|
|Mar 4||Mar 5||Mar 6
Lab: Retina and other Biometrics
|Mar 11||Mar 12||Mar 13
||Mar 14||Mar 15
||Mar 16||Mar 17|
|Mar 18||Mar 19||Mar 20
||Mar 22||Mar 23||Mar 24|
|Mar 25||Mar 26||Mar 27
Lab 8: Speech Processing with Praat
|Mar 30||Mar 31|
|Apr 1||Apr 2||Apr 3
L10: Speech Recognition and Text processing
Lab 9: Sinsum2 and NTLK
|Apr 6||Apr 7|
|Apr 8||Apr 9||Apr 10
L11: Keystroke Recognition and other Behavioral Biometrics
|Apr 11||Apr 12
|Apr 13||Apr 14|
|Apr 15||Apr 16||Apr 17
L12: Hidden Markov Models, Naive Bayes, and other Probabilistic Approaches
Lab 10: NLTK and HMMs with Python for behavioral biometric analysis
|Apr 22||Apr 23||Apr 24
|Apr 29||Apr 30
Term Project Presentations
|May 6||May 7