Lunes 18 de diciembe 14hs, Laboratorio de Software del IIE – Facultad de Ingeniería, J. Herrera y Reissig 565
Tenemos el agrado de invitarlos a la defensa de tesis de maestría de Luis Di Martino : “Outliers in biometrics : An a-contrario approach”
Director de Tesis : Alicia Fernández, Rafael Grompone, Federico Lecumberry y Javier Preciozzi
Tribunal : Rafael Molina (Universidad de Granada, España), Marcelo Fiori y Pablo Muse
A statistical approach is presented based on a well-known a-contrario validation strategy. Techniques based on such framework have been widely used in the fields of image processing and computer vision for the detection and matching of visual features. In this work, the method ability to detect outliers/inliers is exploited to detect when two compared biometric samples correspond to the same person. This method is adapted and applied to each of the usual biometric tasks.
First, it is applied to the task of biometric verification, modeling it as a two- class classification problem. The introduced strategy was validated using different datasets and compared against other state-of-the-art commonly used classification methods. Findings of this work have been presented at the 2014 International Conference on Pattern Recognition Applications and Methods (ICPRAM-2014), by applying the framework to the face recognition problem in particular. An extension of the conference article has been published as a journal article. In this thesis, the presented strategy is reviewed with an experimental evaluation done in several bigger datasets.
Secondly, the a-contrario framework is applied to the identification task. The method is used to validate the confidence of an identification system outputs. What is normally called in the literature as System Response Reliability (SRR). Such problem has been thoroughly studied lately, the key advantages of using such control are analyzed and discussed. The obtained performance is validated on multiple datasets by comparing with other state-of-the-art approaches. This work has been presented on the 2016 International Conference of the Biometrics Special Interest Group (BIOSIG-2016).
Finally, the framework is applied to biometric fusion. The key differences in such scenario and the corresponding proposed framework adaptations are analyzed. The proposed technique is evaluated in both artificially generated as real-scenario datasets. The performance is compared against other state-of-the-art statistically fusion strategies.