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

Abstract—In this paper, we define meta-recognition, a performance prediction method for recognition algorithms, and examine the theoretical basis for its post-recognition 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 non-matches, and automatic algorithm selection or weighting for multi-algorithm fusion. The emerging body of literature on post-recognition 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 pr...

• Type: Journal
• Year: 2011
• Where: PAMI
• Authors: Walter J. Scheirer, Anderson Rocha, Ross J. Micheals, Terrance E. Boult
• Keywords: Computer Networks, Extreme Value Theory, Face Recognition Algorithms, Object Recognition System, PAMI 2011