Biometric recognition, or biometrics, refers to the automatic recognition of a person based on his anatomical or behavioral characteristics. Among the various biometric traits (e.g., face, iris, fingerprint, voice), fingerprint-based authentication has the longest history, and it has been successfully adopted in both forensic and civilian applications. However, the performance of current fingerprint recognition systems is inadequate in the presence of noisy and deformed images, especially when the fingerprint database is very large. Three areas of statistical research are impacted, namely, the analysis of functional data, multivariate dependence, and spatial and general point processes. The proposed research will develop and utilize a Bayesian framework and related computational schemes for inference. This framework will be used to address following specific problems: fingerprint feature detection, modeling noisy and deformed images, fingerprint individuality (i.e., uniqueness) assessment, and effective distributional representations for information fusion (multi-biometrics).
Advances in fingerprint capture technology have resulted in new large scale civilian applications such as the US-VISIT program. However, these systems still encounter difficulties due to the effects of biometric variability present in operating environments and the massive number of comparisons that have to be executed in each identification task. The proposed model-based methods are likely to improve the effectiveness of various fingerprint processing tasks which will eventually yield improved identification performance in real operating environments. Further, this research will impact how fingerprint evidence is reported and used for the identification of suspects. The proposed research increases the role of statistics in important computer science and engineering applications, and provides an impetus for inter-disciplinary research and synergistic activities. Both undergraduate and graduate students working on the proposed topics will develop the analytical and computing skills required to perform scientific research. In this way, the proposed research helps in the creation of future scientists to work in the emerging and critical field of biometric recognition.
