Cancelable biometrics realization with multispace random projections.
Biometric characteristics cannot be changed; therefore, the loss of privacy is permanent if they are ever compromised. This paper presents a two-factor cancelable formulation, where the biometric data are distorted in a revocable but non-reversible manner by first transforming the raw biometric data into a fixed-length feature vector and then projecting the feature vector onto a sequence of random subspaces that were derived from a user-specific pseudorandom number (PRN). This process is revocable and makes replacing biometrics as easy as replacing PRNs. The formulation has been verified under a number of scenarios (normal, stolen PRN, and compromised biometrics scenarios) using 2400 Facial Recognition Technology face images. The diversity property is also examined.
Teoh
Andrew Beng Jin
AB
Biometrics Engineering Research Center (BERC), Yonsei University, Seoul 120-749, Korea. bjteoh@ieee.org
Yuang
Chong Tze
CT
D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D016494:Computer Security; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity