Automatic face recognition with well-calibrated confidence measures

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Automatic face recognition with well-calibrated confidence measures

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Automatic face recognition with well-calibrated confidence measures

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/LO1506" target="_blank" >LO1506: Podpora udržitelnosti centra NTIS - Nové technologie pro informační společnost</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Machine Learning

ISSN

0885-6125

e-ISSN

—

Svazek periodika

108

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

24

Strana od-do

511-534

Kód UT WoS článku

000459945900007

EID výsledku v databázi Scopus

2-s2.0-85052496742