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Past Projects

General Biometrics

Biometric Template Selection and Update

The matching accuracy of a biometrics-based authentication system is affected by the stability of the biometric data associated with the users of the system. But due to factors such as improper interaction with the sensor, variations in environmental factors and temporary alterations of the biometric trait itself, biometric data have a large intra-class variability. Hence biometric template data can be significantly different from samples obtained during authentication. To alleviate this problem, multiple templates, instead of a single template, per user can be stored in a biometric database. In this project, we study techniques to automatically select and update templates by considering system performance along with storage and computational overheads associated with multiple templates.

U. Uludag, A. Ross and A. K. Jain, " Biometric Template Selection and Update: A Case Study in Fingerprints," Pattern Recognition, Vol. 37, No. 7, pp. 1533-1542, July 2004.

A. K. Jain, U. Uludag and A. Ross, " Biometric Template Selection: A Case Study in Fingerprints", Proc. of 4th Int'l Conf. on Audio- and Video-Based Biometric Person Authentication (AVBPA), pp. 335-342, Guildford, UK, June 9-11, 2003.

Image Quality

Image quality is one of the variables that has the largest effect on biometric system accuracy and is the major cause of high false reject rates (FRR) and failure to enroll (FTE) rates. Automatic image quality measures can be developed to (i) provide real-time feedback to reduce the number of poor quality submissions to the system, (ii) predict and improve the authentication performance using quality-dependent thresholds, and (iii) generate quality-based user (or modality) specific weights for multi-biometric fusion.

Y. Chen, S. Dass and A. Jain, " Localized Iris Image Quality Using 2-D Wavelets", Proc. of International Conference on Biometrics (ICB), pp. 373-381, Hong Kong, January, 2006.

J. Fierrez-Aguilar, Y. Chen, J. Ortega-Garcia and A. K. Jain, " Incorporating image quality in multi-algorithm fingerprint verification", Proc. of International Conference on Biometrics (ICB), pp. 213-220, Hong Kong, January, 2006.

Y. Chen, S. Dass and A. Jain, " Fingerprint Quality Indices for Predicting Authentication Performance", Proc. of Audio- and Video-based Biometric Person Authentication (AVBPA), pp. 160-170, Rye Brook, NY, July 2005.

K. Nandakumar, Y. Chen, S.C. Dass and A.K. Jain, " Quality-based Score Level Fusion in Multibiometric Systems", Proc. of International Conference on Pattern Recognition (ICPR), Vol. 4, pp. 473-476, Hong Kong, August, 2006.

Sample Size Requirement for Performance Evaluation

Authentication systems based on biometric features (e.g., fingerprint impressions, iris scans, human face images, etc.) are increasingly gaining widespread use and popularity. Often, vendors and owners of these commercialbiometric systems claim impressive performance that is estimated based on some proprietary data. In such situations, there is a need to independently validate the claimed performance levels. System performance is typically evaluated by collecting biometric templates from n different subjects, and for convenience, acquiring multiple instances of the biometric for each of the n subjects. Very little work has been done in (i) constructing confidence regions based on the ROC curve for validating the claimed performance levels, and (ii) determining the required number of biometric samples needed to establish confidence regions of pre-specified width for the ROC curve. To simplify the analysis that address these two problems, several previous studies have assumed that multiple acquisitions of the biometric entity are statistically independent. This assumption is too restrictive and is generally not valid. We have developed a validation technique based on multivariate copula models for correlated biometric acquisitions. Based on the same model, we also determine the minimum number of samples required to achieve confidence bands of desired width for the ROC curve. We illustrate the estimation of the confidence bands as well as the required number of biometric samples using a fingerprint matching system that is applied on samples collected from a small population.

S. C. Dass, Y. Zhu and Anil K. Jain, " Validating a Biometric Authentication System: Sample Size Requirements", IEEE Transactions of Pattern Analysis and Machine Intelligence, pp. 1902-1913, 2006.


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