Skip to Main Content
The generation of changeable and privacy-preserving biometric templates is important for the pervasive deployment of biometric technology in a wide variety of applications. This paper presents a systematic analysis of random transformation-based methods for addressing the changeability and privacy problems in biometrics-based verification systems. The proposed methods transform the original biometric feature vectors using random transformations, and the sorted index numbers (SIN) of the resulting vectors in the transformed domain are stored as the biometric templates. Three types of random transformations, namely, random additive transform, random multiplicative transform, and random projection, are discussed and analyzed. The random transformations, in combination with the SIN approach, constitute repeatable and noninvertible transformations; hence, the generated templates are changeable and provide privacy protection. The effectiveness of the proposed methods is well supported by both detailed analysis and extensive experimentation on a face verification problem.