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Biometrics offers usability advantages over traditional token and password based authentication schemes, but raises privacy and security concerns. When compromised, credit cards and passwords can be revoked or replaced while biometrics are permanently associated with a user and cannot be replaced. Cancelable biometrics attempts to solve this by constructing revocable biometric templates. We present several constructs for cancelable templates using feature domain transformations and empirically examine their efficacy. We also present a method for accurate registration which is a key step in building cancelable transforms. The overall approach has been tested using large databases and our results demonstrate that without losing much accuracy, we can build a large number of cancelable transforms for fingerprints