Automated Border Control (ABC) systems are being increasingly used to perform a fast, accurate, and reliable verification of the travelers' identity. These systems use biometric technologies to verify the identity of the person crossing the border. In this context, fingerprint verification systems are widely adopted due to their high accuracy and user acceptance. Matching score normalization methods can improve the performance of fingerprint recognition in ABC systems and mitigate the effect of non-idealities typical of this scenario without modifying the existing biometric technologies. However, privacy protection regulations restrict the use of biometric data captured in ABC systems and can compromise the applicability of these techniques. Cohort score normalization methods based only on impostor scores provide a suitable solution, due to their limited use of sensible data and to their promising performance. In this paper, we propose a privacy-compliant and adaptive normalization approach for enhancing fingerprint recognition in ABC systems. The proposed approach computes cohort scores from an external public dataset and uses computational intelligence to learn and improve the matching score distribution. The use of a public dataset permits to apply cohort normalization strategies in contexts in which privacy protection regulations restrict the storage of biometric data. We performed a technological and a scenario evaluation using a commercial matcher currently adopted in real ABC systems and we used data simulating different conditions typical of ABC systems, obtaining encouraging results.
Enhancing fingerprint biometrics in Automated Border Control with adaptive cohorts / A. Anand, R. Donida Labati, A. Genovese, E. Muñoz Ballester, V. Piuri, F. Scotti, G. Sforza - In: Computational Intelligence (SSCI), 2016 IEEE Symposium Series on[s.l] : IEEE, 2016 Dec. - ISBN 9781509042401. - pp. 1-8 (( convegno SSCI tenutosi a Athens nel 2016.
|Titolo:||Enhancing fingerprint biometrics in Automated Border Control with adaptive cohorts|
|Parole Chiave:||score normalization; recognition; systems; fusion|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Progetto:||Enforceable Security in the Cloud to Uphold Data Ownership|
ABC GATES FOR EUROPE
|Data di pubblicazione:||dic-2016|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/SSCI.2016.7850073|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|