Issues and Challenges on Multimodal Biometrics

Issues and Challenges on Multimodal Biometrics

• Friday 16, 14-16
• Monday 19, 10-12
• Wednesday 21, 10-12
• Friday 23, 10-12

• Friday 16: Mocci Classroom, A Building
• Moonday 19, Wednesday 21, Friday 23: Meeting Room, B Building

This course aim to arm the participants by hand-crafing a Bayesian classifier using both generative and discriminative approaches. Advanced topics such as dealing with missing values and Bayesian network are also covered. These techniques enable one to solve many machine-learning problems where training data is an integral part of the system design. Practical applications are illustrated via multimodal information fusion in biometrics.

The course (8 lecture hours) will be roughly structured as shown below:

• Lecture 1: Decision theory, generative and discriminative classifiers, Naive Bayes. Slides.
• Lecture 2: Error prediction in multimodal information fusion. Slides.
  
• Lecture 3: User-specific score normalization and fusion. Slides
  
• Lecture 4: Quality-based fusion. Slides
  

Lectures 2-4 have example applications applied to biometrics.

Course Bibliography.

• Web Page
• Notes

Assignement

• Instructions.txt
• Data.txt