Cancellable biometrics has been a challenging and essential approach to protect the privacy of biometric templates. Multiple Random Projections (MRP) is our formerly presented two-factor cancellable formulation. In that method, the biometric data is changed in a revocable but noninvertible manner by projecting every fixed length feature vector (extracted from the raw biometrics) onto a user-specific random subspace. In this paper, we propose a variant of MRP, namely Multiple Random Projections-Support Vector Machine (MRP-SVM). The MRP's template protection characteristics are inherited by MRP-SVM due to existence of the property of dot product and non-linear kernel. Furthermore, the verification performance is improved. This approach is verified using the touch-less based acquired fingerprint images. Touch-less based acquired images are free from latent fingerprint issues that can lead to fraudulent use. Hence, the security and privacy protection of fingerprint biometric templates is consolidated by the cancellable biometrics approach.