Random Slope method for generation of cancelable biometric features

Cancelable biometric templates are transformed versions of original biometric templates used for authentication purposes. The transformation functions should fulfill the important template protection criteria of diversity, revocability, non-invertibility, and performance. Although there exists a number of transformation techniques, yet many of these techniques fail to meet security and privacy requirements in the stolen token scenario and tend to become invertible with degraded performance. The work proposes a novel cancelable biometric technique named as 'Random Slope' method for generating secure, revocable, and non-invertible templates. Two approaches (RS-V1 and RS-V2) developed using the proposed concept not only fulfill the important cancelability criteria, but also provide dimensionality reduction upto 75%. The performances of the proposed approaches are experimentally verified for various biometric modalities such as visible and thermal face, palmprint, palmvein, and fingervein. As compared to some state of the art template transformation schemes, the proposed RS-V1 and RS-V2 approaches establish their reliability and effectiveness by performing better than these existing techniques with significant reduction in dimensions. (C) 2018 Elsevier B.V. All rights reserved.