Predicting Face Recognition Performance in Unconstrained Environments

被引：1

作者：

Phillips, P. Jonathon ^{[1
]}

Yates, Amy N. ^{[1
]}

Beveridge, J. Ross ^{[2
]}

Givens, Geof ^{[3
]}

机构：

[1] NIST, Gaithersburg, MD 20899 USA

[2] Colorado State Univ, Dept Comp Sci, Ft Collins, CO 80523 USA

[3] Givens Stat Solut LLC, Ft Collins, CO USA

来源：

2017 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW) | 2017年

关键词：

D O I：

10.1109/CVPRW.2017.83

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

While face recognition algorithms perform under many different unconstrained conditions, predicting this performance is not possible when a new location is introduced. Analyzing the impostor distribution of the videos of the Point-and-Shoot Challenge (PaSC) as well as its relationship to the genuine match distribution, we show that there is large variation in the false accept rate over the impostor distribution, demonstrate there is a correlation between changes in the verification and false accept rates over factor, and using this, present a method for predicting the performance of an algorithm using only unlabeled data for a new location.

引用

页码：557 / 565

页数：9

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