Interactive quality-driven feedback for biometric systems

The application of biometric technology has so far been top-down, driven by governments and law enforcement agencies. The low demand of this technology from the public, despite its many advantages compared to the traditional means of authentication is probably due to the lack of human factor considerations in the design process. In this work, we propose a guideline to design an interactive quality-driven feedback mechanism. The mechanism aims to improve the quality of biométrie samples during the acquisition process by putting in place objective assessment of the quality and feeding this information back to the user instantaneously, thus eliminating subjective quality judgement by the user. We illustrate the feasibility of the design methodology using face recognition as a case study. Preliminary results show that the methodology can potentially increase efficiency, effectiveness and accessibility of a biométrie system.

[1]  William J. Christmas,et al.  General Pose Face Recognition Using Frontal Face Model , 2006, CIARP.

[2]  Vincent G. Duffy,et al.  The Effects of Human Interaction on Biometric System Performance , 2007, HCI.

[3]  Mary F. Theofanos,et al.  Usability and Biometrics: Ensuring Successful Biometric Systems | NIST , 2008 .

[4]  Jiri Matas,et al.  Weighted Sampling for Large-Scale Boosting , 2008, BMVC.

[5]  Bruce A. Draper,et al.  Repeated Measures GLMM Estimation of Subject-Related and False Positive Threshold Effects on Human Face Verification Performance , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[6]  Roberto Paredes,et al.  Simultaneous learning of a discriminative projection and prototypes for Nearest-Neighbor classification , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Erik Hjelmås,et al.  Face Detection: A Survey , 2001, Comput. Vis. Image Underst..

[8]  Sébastien Marcel,et al.  Local binary patterns as an image preprocessing for face authentication , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[9]  Mohan M. Trivedi,et al.  Head Pose Estimation in Computer Vision: A Survey , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Bruce A. Draper,et al.  A meta-analysis of face recognition covariates , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[11]  Krzysztof Kryszczuk,et al.  What do quality measures predict in biometrics? , 2008, 2008 16th European Signal Processing Conference.

[12]  B. Stanton,et al.  Biometric Systematic Uncertainty and the User , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[13]  Andy Adler,et al.  Human Vs. Automatic Measurement of Biometric Sample Quality , 2006, 2006 Canadian Conference on Electrical and Computer Engineering.

[14]  Elham Tabassi,et al.  Performance of Biometric Quality Measures , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  B. Martin,et al.  Quality Assessment of Facial Images , 2006, 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference.

[16]  Tena Rodriguez,et al.  3D face modelling for 2D+3D face recognition , 2007 .

[17]  Thomas Vetter,et al.  Face Recognition Based on Fitting a 3D Morphable Model , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[19]  Jean Scholtz,et al.  Does habituation affect fingerprint quality? , 2006, CHI Extended Abstracts.