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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.
Weibull distribution, content-based retrieval, face recognition, fingerprint identification, image fusion, image retrieval, object recognition, content-based image retrieval system, meta-recognition, performance prediction method, postrecognition score analysis, statistical extreme value theory, statistical EVT, automatic threshold selection, automatic algorithm selection, multialgorithm fusion, biometrics, image quality metrics, Weibull distribution, face recognition algorithm, fingerprint recognition algorithm, SIFT-based object recognition system, Portfolios, Probes, Weibull distribution, Prediction algorithms, Data models, Image recognition, Face recognition, extreme value theory., Meta-recognition, performance modeling, multialgorithm fusion, object recognition, face recognition, fingerprint recognition, content-based image retrieval, similarity scores
"Meta-Recognition: The Theory and Practice of Recognition Score Analysis", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 1689-1695, August 2011, doi:10.1109/TPAMI.2011.54
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