A general probability framework for improving similarity based approaches for face verification

This paper introduces a probability model for face verification, aiming at improve various similarity comparison approaches transplanted directly from face identification algorithms. Experiences demonstrate that, when embedded with a few well known subspace based similarity comparison approaches, our probability model can efficiently reduce the error rates in face verification tasks.