On-Line Fingerprint Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal verification based on extraction and characterisation of retinal feature points
Journal of Visual Languages and Computing
A Biometric Menagerie Index for Characterising Template/Model-Specific Variation
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Retinal verification using a feature points-based biometric pattern
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Cost curve analysis of biometric system performance
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
A Comparative Study of Palmprint Recognition Algorithms
ACM Computing Surveys (CSUR)
Gait verification using knee acceleration signals
Expert Systems with Applications: An International Journal
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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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 commercial biometric 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 1) constructing confidence regions based on the ROC curve for validating the claimed performance levels and 2) determining the required number of biometric samples needed to establish confidence regions of prespecified 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.