The purpose of this paper is to introduce an effective and structured methodology for carrying out a biometric system sensitivity analysis. The goal of sensitivity analysis is to provide the researcher/developer with the insight and understanding of the key factorsalgorithmic, subject-based, procedural, image quality, environmental, among othersthat affect the matching performance of the biometric system under study. This proposed methodology consists of two steps: 1) the design and execution of orthogonal fractional factorial experiment designs which allow the scientist to efficiently investigate the effect of a large number of factorsand interactionssimultaneously, and 2) the use of a select set of statistical data analysis graphical procedures which are fine-tuned to unambiguously highlight important factors, important interactions, and locally-optimal settings. We illustrate this methodology by application to a study of VASIR (Video-based Automated System for Iris Recognition)NIST iris-based biometric system. In particular, we investigated k = 8 algorithmic factors from the VASIR system by constructing a ((2)^(6-1) x (3)^1 x (4)^1) orthogonal fractional factorial design, generating the corresponding performance data, and applying an appropriate set of analysis graphics to determine the relative importance of the eight factors, the relative importance of the 28 two-term interactions, and the local best-settings of the eight algorithms. The results showed that VASIRs performance was primarily driven by six factors out of the eight, along with four two-term interactions. A virtue of our two-step methodology is that it is systematic and general, and hence may be applied with equal rigor and effectiveness to other biometric systems, such as fingerprints, face, voice, DNA, etc.
Citation: Computer Vision and Image Understanding
Pub Type: Journals
sensitivity analysis, iris recognition, biometrics, experiment design, VASIR, video-based biometric system, orthogonal, fractional factorial, characterization, uncertainty, factor effects, interaction effects, optimization, main effects plots, interaction plots