Cancelable Face Recognition Using Random Multiplicative Transform

The generation of cancelable and privacy preserving biometric templates is important for the pervasive deployment of biometric technology in a wide variety of applications. This paper presents a novel approach for cancelable biometric authentication using random multiplicative transform. The proposed method transforms the original biometric feature vector through element-wise multiplication with a random vector, and the sorted index numbers of the resulting vector in the transformed domain are stored as the biometric template. The changeability and privacy protecting properties of the generated biometric template are analyzed in detail. The effectiveness of the proposed method is well supported by extensive experimentation on a face verification problem.