Performance Evaluation of Biometric Template Update

Template update allows to modify the biometric reference of a user while he uses the biometric system. With such kind of mechanism we expect the biometric system uses always an up to date representation of the user, by capturing his intra-class (temporary or permanent) variability. Although several studies exist in the literature, there is no commonly adopted evaluation scheme. This does not ease the comparison of the different systems of the literature. In this paper, we show that using different evaluation procedures can lead in different, and contradictory, interpretations of the results. We use a keystroke dynamics (which is a modality suffering of template ageing quickly) template update system on a dataset consisting of height different sessions to illustrate this point. Even if we do not answer to this problematic, it shows that it is necessary to normalize the template update evaluation procedures.

[1]  Christophe Rosenberger,et al.  Analysis of template update strategies for keystroke dynamics , 2011, 2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM).

[2]  Gian Luca Marcialis,et al.  Adaptive Biometric Systems That Can Improve with Use , 2008 .

[3]  Gian Luca Marcialis,et al.  Self adaptive systems: An experimental analysis of the performance over time , 2011, 2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM).

[4]  Anil K. Jain,et al.  Template Adaptation based Fingerprint Verification , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[5]  Stan Z. Li,et al.  Advances in Biometrics, International Conference, ICB 2007, Seoul, Korea, August 27-29, 2007, Proceedings , 2007, ICB.

[6]  Josef Kittler,et al.  A Method for Estimating Authentication Performance over Time, with Applications to Face Biometrics , 2007, CIARP.

[7]  Roy A. Maxion,et al.  Comparing anomaly-detection algorithms for keystroke dynamics , 2009, 2009 IEEE/IFIP International Conference on Dependable Systems & Networks.

[8]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[9]  Karl Ricanek,et al.  MORPH: a longitudinal image database of normal adult age-progression , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[10]  Chandra Kambhamettu,et al.  VADANA: A dense dataset for facial image analysis , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[11]  A. Martínez,et al.  The AR face databasae , 1998 .

[12]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Juan J. Igarza,et al.  MCYT baseline corpus: a bimodal biometric database , 2003 .

[14]  Christophe Rosenberger,et al.  GREYC keystroke: A benchmark for keystroke dynamics biometric systems , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[15]  Andrzej Drygajlo,et al.  Q-stack aging model for face verification , 2009, 2009 17th European Signal Processing Conference.

[16]  Patrick Bours,et al.  How to comprehensively describe a biometric update mechanisms for keystroke dynamics , 2011, 2011 Third International Workshop on Security and Communication Networks (IWSCN).

[17]  Bernadette Dorizzi,et al.  On assessing the robustness of pen coordinates, pen pressure and pen inclination to time variability with personal entropy , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[18]  Gian Luca Marcialis,et al.  Replacement Algorithms for Fingerprint Template Update , 2008, ICIAR.

[19]  Massimo Tistarelli,et al.  Advances in Biometrics , 2009, Lecture Notes in Computer Science.

[20]  Christophe Rosenberger,et al.  Keystroke Dynamics Overview , 2011 .