Investigating fusion approaches in multi-biometric cancellable recognition

• Published in: Expert systems with applications 2013-05, Vol.40 (6), p.1971-1980
• Main Authors: Canuto, Anne M.P., Pintro, Fernando, Xavier-Junior, João C.
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Biometric template protection; Biometrics; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Exact sciences and technology; Expert systems; Management; Memory and file management (including protection and security); Memory organisation. Data processing; Multibiometric recognition; Pattern recognition. Digital image processing. Computational geometry; Recognition; Representations; Software; Transformations; Transforms; Voice
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2012.10.002

► We analyzed fusion approaches for cancellable multi-biometric data, with ensembles. ► We adapted three transformation functions (FTs) to be used with voice and iris data. ► We find that individual FTs decrease the accuracy of the voice and iris dataset. ► The combination of transformation functions increased the accuracy of the ensembles. ► The statistical analysis proved the good results reached by combining all three FTs. Cancellable biometrics has recently been introduced in order to overcome some privacy issues about the management of biometric data, aiming to transform a biometric trait into a new but revocable representation for enrolment and identification (verification). Therefore, a new representation of original biometric data can be generated in case of being compromised. Additionally, the use multi-biometric systems are increasingly being deployed in various biometric-based applications since the limitations imposed by a single biometric model can be overcome by these multi-biometric recognition systems. In this paper, we specifically investigate the performance of different fusion approaches in the context of multi-biometrics cancellable recognition. In this investigation, we adjust the ensemble structure to be used for a biometric system and we use as examples two different biometric modalities (voice and iris data) in a multi-biometrics context, adapting three cancellable transformations for each biometric modality.