Feature fusion for template stability in biometric cryptosystems. An application to face biometrics based on eigen-models

User's privacy protection is a very important issue which can seriously affect the usability of a biometric system, and prevent its successful establishment. In order to perform people verification while providing security and privacy to the employed characteristics, several biometric template protection schemes have been recently proposed. Unfortunately, when deploying a biometric cryptosystem, a significant intra-class variability, combined with the lack of enrollment data for estimating the templates' statistics, may prevent the users' verification due to the limits on error correction capability of the employed codes. In this paper we propose a feature-level fusion technique which can significantly reduce the intra-class variability of feature-based templates, thus allowing to reach low false recognition rates in protected systems. The benefits of the proposed approach are evaluated over a novel video-based face verification system relying on Universal Background Models and adapted user model eigen-projections. Tests over the BANCA database show good performance of the proposed features for face verification, and improved template protection when the proposed feature fusion approach is applied.