Adaptive biometric systems
Template update and selection algorithms are aimed to make the biometric system “adaptive” to the intra-class variations of the input data.
Recently, novel solutions have been introduced in the form of template updating where the classifiers adapts itself to the intra-class variations based on learning methodologies like supervised or semi-supervised learning. Although these methods are promising, the state-of-art related to them is still in its infancy.
Our contributions to the state-of-the-art are as follows:
- Introduction of semi-supervised learning techniques to template update problems.
- A critical review of template update methods to biometrics. The aim is to highlight current state-of-the-art with respect to current approaches, in particular, the learning methodology adopted and experimental evaluation followed.
- Proposal of novel approaches to template update, namely, self update, graph-mincut and co-update ones.
- Replacement algorithms
- Investigation of pros and cons of several template update methods as function of the given user population.
People working on this topic:
- Biagio Freni
Gian Luca Marcialis