Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

A Baseline for Assessing Biometrics Performance Robustness: A Case Study across Seven Iris Datasets

Published

Author(s)

Yooyoung Lee, James J. Filliben, Ross J. Micheals, Michael D. Garris, P J. Phillips

Abstract

We examine the robustness of algorithm performance over multiple datasets collected with different sensors. This study provide insight as to whether an algorithm performance derived from traditional controlled environment studies will robustly extrapolate to more challenging stand-off/real-world environments. We argue that a systematic methodology is critical in assuring the validity of algorithmic conclusions over the broader arena of applications. We present a structured evaluation protocol and demonstrate its utility by comparing the performance of the open-source algorithm over seven datasets, spanning six different sensors (three stationary, one handheld, and two stand-off types). We also provide results for the ranking of the seven datasets measured by four performance metrics. Finally, we compare our protocol-based ranking with a parallel ranking based on an independent survey results from a collection of biometrics experts, with high correlation between the two rankings being demonstrated.
Proceedings Title
The IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS 2013)
Conference Dates
September 29-October 2, 2013
Conference Location
washington, DC

Keywords

biometrics, iris recognition, robustness, algorithmic robustness, multiple datasets, data diversity, performance evaluation

Citation

Lee, Y. , Filliben, J. , Micheals, R. , Garris, M. and Phillips, P. (2013), A Baseline for Assessing Biometrics Performance Robustness: A Case Study across Seven Iris Datasets, The IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS 2013), washington, DC, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=913895 (Accessed December 4, 2021)
Created November 7, 2013, Updated February 19, 2017