Template protection for HMM-based on-line signature authentication

The security of biometric data is a very important issue in the deployment of biometric-based recognition systems. In this paper, we propose a signature-based biometric authentication system, where signal processing techniques are applied to the acquired on-line signature in order to generate protected templates, from which retrieving the original data is computationally as hard as randomly guessing them. A hidden Markov model (HMM)-based matching strategy is employed to compare the transformed signatures. The proposed protected authentication system generates a score as the result of the matching process, thus allowing to implement protected multibiometric recognition systems, through the application of score-fusion techniques. The experimental results show that, at the cost of only a slight performance reduction, the desired protection for the employed biometric templates can be properly achieved.