July 13–17, 2020
Vitomir Štruc (UL FE), Rudolf Haraksim (JRC), Blaž Meden, Matej Vitek, Žiga Emeršič, Peter Peer
Bachelor’s or Master’s degree
Basic programming knowledge
B2 English level
About the course
Content of this course is based on principles of computer vision that represent a basis for the majority of biometric systems, where the input is an image. As a part of this course, the content will be delivered theoretically and practically through use-cases and the state-of-the-art implementations, including deep learning.
Aims of the course
To present biometrics from the basics, right to the smallest details in implementations and state-of-the-art research.
Why should you attend this course?
To learn about the all-important field of biometrics and get advice on your specific biometric-related challenges.
After the course, you will:
- know the key properties of each biometric modality,
- understand all the steps of each biometric system,
- know to define needed properties to introduce a biometric system into a real-life environment,
- know how to properly measure and compare the performance of biometric systems,
- understand the key terminology of each modality and the use of it in the real-life systems,
- understand the advantages and shortcomings of specific modalities,
- know how to fuse modalities to achieve better performance of biometric systems,
- know key algorithms of computer vision in biometrics.