Semester

Summer

Date of Graduation

2004

Document Type

Thesis

Degree Type

MS

College

Statler College of Engineering and Mineral Resources

Department

Lane Department of Computer Science and Electrical Engineering

Committee Chair

Xin Li.

Abstract

The goal of any biometric recognition algorithm has been to target 100% recognition rate. Even a failure of 0.1% of the cases may be disastrous to the overall performance of the algorithm given the nature of the application. The performance of the recognition module is dependent directly on the quality of the images fed to this algorithm. It is no wonder thus that enhancing these images before they are fed into the recognition algorithm has gained importance in the recent years.;In this work, we develop a super-resolution method with a specific goal to improve the resolution of biometric images. This method can be used as a preprocessing step in a variety of biometric applications including fingerprint enhancement with the purpose of detecting pores on fingerprints, compression of biometric data using distributed coding, and increasing the resolution of facial images with the purpose to improve performance of the face based recognition systems. This preprocessing step is of great importance since it admits the use of low quality of-the-shelf sensors for data collection in place of high resolution expensive sensors to capture biometric images. (Abstract shortened by UMI.).

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