Automatic face recognition with well-calibrated confidence measures

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43954846" target="_blank" >RIV/49777513:23520/19:43954846 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s10994-018-5756-7" target="_blank" >https://link.springer.com/article/10.1007/s10994-018-5756-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10994-018-5756-7" target="_blank" >10.1007/s10994-018-5756-7</a>

Result language

angličtina

Original language name

Automatic face recognition with well-calibrated confidence measures

Original language description

Many Automatic face recognition (AFR) methods achieve a high recognition accuracy when the environment is well-controlled. In the case of moderately controlled or fully uncontrolled environments however, the performance of most techniques is dramatically reduced. As a result, the provision of some kind of indication of the likelihood of a recognition being correct is a desirable property of AFR techniques. This work investigates the application of the conformal prediction (CP) framework for extending the output of AFR techniques with well-calibrated measures of confidence. In particular we combine CP with one classifier based on POEM descriptors, one classifier based on SIFT descriptors, and a weighted combination of the similarities computed by the two. We examine and compare the performance of five nonconformity measures.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/LO1506" target="_blank" >LO1506: Sustainability support of the centre NTIS - New Technologies for the Information Society</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Machine Learning

ISSN

0885-6125

e-ISSN

—

Volume of the periodical

108

Issue of the periodical within the volume

3

Country of publishing house

US - UNITED STATES

Number of pages

24

Pages from-to

511-534

UT code for WoS article

000459945900007

EID of the result in the Scopus database

2-s2.0-85052496742