Learning small gallery size for prediction of recognition performance on large populations

作者：

Highlights：

• Prediction model combines hypergeometric distribution model with a binomial model.

• Optimal size of a small gallery is found by an iterative learning process.

• Chernoff and Chebychev inequalities are used to determine small gallery size in theory.

• Experimental results are shown on a challenging data set of fingerprints (NIST-4).

摘要

Highlights•Prediction model combines hypergeometric distribution model with a binomial model.•Optimal size of a small gallery is found by an iterative learning process.•Chernoff and Chebychev inequalities are used to determine small gallery size in theory.•Experimental results are shown on a challenging data set of fingerprints (NIST-4).

论文关键词：Biometrics,Distortion modeling,Learning,Optimal small gallery size,Performance bounds,Performance prediction

论文评审过程：Received 30 October 2012, Revised 8 May 2013, Accepted 20 May 2013, Available online 7 June 2013.

论文官网地址：https://doi.org/10.1016/j.patcog.2013.05.024