Liveness Detection and Robust Recognition in Iris and Fingerprint Biometric Systems

Abstract

Biometrics represents a return to a traditional way of identifying someone relying on what that person
is instead of what that person knows or owns. Even though the significant amount of research
that has been done in this field, there is still much to do as new emerging scenarios of application
appear everyday. Biometric recognition systems are no longer restricted to forensic investigation
or control management of employees. They have been gaining a visibility and applicability in
daily use devices which reinforces their usability in all aspects of our day to day life.
With this spread of biometric applications, nowadays commonly found in our laptops, our
smart phones, some bank management services and airport custom services, a necessity for improved
security also is rising. The importance of protecting our identity and our data has become
crucial as our devices are filled with sensible information of many kinds.
Therefore the presentation attack or liveness detection methods as countermeasures against
spoofing attacks are more important than ever. New methods should be developed which address
the new acquisition scenarios and which deal with the increased noise in the biometric data collected.
Its of utmost importance to develop robust liveness detection methods. In particular, we
worked on iris and fingerprint. These two biometric traits are very often chosen against others
due to its characteristics. Among the objectives of this thesis were the purpose of making contributions
in iris and fingerprint liveness detection proposing novel approaches whether from the
imaging scenarios perspective, in the case of iris, or from the classification approach, in the case
of fingerprint. Contributions were made regarding both traits, that exceeded the state-of-the art
and resulted in both conferences and journal publications.
Not only the spoofing attacks concern the biometric researchers but also the ability of the methods
to deal with the noisy data. Therefore, the development of robust methods that overcome the
compromised quality of data is a necessity of biometric research of nowadays. Therefore, another
objective was to contribute to the fingerprint recognition problem developing robust methods to
minutiae extraction. The work developed resulted in a proposed method for fingerprint orientation
map estimation and a fingerprint image enhancement that over performed existing ones.
This work aimed and succeeded to propose robust and realistic methods in both the iris and
fingerprint liveness detection problem as well as in some steps of fingerprint recognition. It has to
be noted that the focus of attention of this work was the quality of data and not the computational
efficiency, therefore this one should have to be addressed if an application of the proposed methods
to a real-world scenario was aimed.
Another objective was to create new databases and promote common platforms of evaluation
of methods such as biometric competitions. Therefore, along the work developed, two biometric
databases were constructed and two biometric competitions were organized. Both databases had a
strong impact in the research community and they continue to be disseminated. Publications using
these benchmark datasets are numerous and continue to appear regularly.