Improving performance and security of biometrics using efficient and stable random projection techniques

The challenges in biometrics research activities have expanded recently to include the maintenance of security and privacy of biometric templates beside the traditional work to improve accuracy, speed, and robustness. Revocable biometric templates and biometric cryptosystems have been developed as template protection measures. Revocability means that biometric templates could be revoked in the same way as lost or stolen credit cards are, while biometric cryptosystems aim to generate biometric keys and hashes to be used as proof of identity. Recently developed biometric protection schemes involve the use of random projections (RP) onto secret personalised domains. In this paper, we propose a novel and efficient orthonormal RP scheme to be used for the generation of revocable biometrics. We shall demonstrate the result of applying our RP transforms on face biometrics and compare its efficiency with that of the widely used RP technique based on the Gram-Schmidt process. We shall also present the results of experimental work on recognition accuracy before and after applying the proposed transform on feature vectors that are generated by wavelet transformed face images. Some security analysis of our scheme will also be presented.