Meta-Recognition: The Theory and Practice of Recognition Score Analysis

Author(s)

Scheirer, Walter J. ^[1] ; Rocha, Anderson ^[2] ; Micheals, Ross J. ^[3] ; Boult, Terrance E. ^[1]

Affiliation

^[1] Univ Colorado, Dept Comp Sci, Colorado Springs, CO 80918 - USA

^[2] Univ Estadual Campinas, IC, BR-13084971 Campinas, SP - Brazil

^[3] NIST, Gaithersburg, MD 20899 - USA

• Document type: Journal article
• Source: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; v. 33, n. 8, p. 1689-1695, AUG 2011.
• Web of Science Citations: 40

Abstract

In this paper, we define meta-recognition, a performance prediction method for recognition algorithms, and examine the theoretical basis for its postrecognition score analysis form through the use of the statistical extreme value theory (EVT). The ability to predict the performance of a recognition system based on its outputs for each match instance is desirable for a number of important reasons, including automatic threshold selection for determining matches and nonmatches, and automatic algorithm selection or weighting for multi-algorithm fusion. The emerging body of literature on postrecognition score analysis has been largely constrained to biometrics, where the analysis has been shown to successfully complement or replace image quality metrics as a predictor. We develop a new statistical predictor based upon the Weibull distribution, which produces accurate results on a per instance recognition basis across different recognition problems. Experimental results are provided for two different face recognition algorithms, a fingerprint recognition algorithm, a SIFT-based object recognition system, and a content-based image retrieval system. (AU)

• FAPESP's process: 10/05647-4 - Digital forensics: collection, organization, classification and analysis of digital evidences
• Grantee: Anderson de Rezende Rocha
• Support Opportunities: Research Grants - Young Investigators Grants