In this paper, a generic cancellable biometrics formulation is proposed by first transforming the raw biometrics data into a fixed length feature vector, then subsequently re-projecting the feature vector onto a sequence of random subspaces specified by the tokenised random vectors. Since random subspace is user-specific, the formulation can be extended to multiple random subspaces for different individuals to amplify the interclass variation whilst maintain the intra-class variation in biometrics verification setting. The privacy invasion and non-revocable problems in biometrics could be resolved by revocation of resulting feature through the random subspace replacement. This formulation furthermore enhances recognition effectiveness as arising from the Multispace Random Projections of biometric and external random inputs.