Learning of a Robusted Nearest Neighbor Classifier Using Multiple Training Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU120033" target="_blank" >RIV/00216305:26220/16:PU120033 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/7502701" target="_blank" >https://ieeexplore.ieee.org/document/7502701</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IWSSIP.2016.7502701" target="_blank" >10.1109/IWSSIP.2016.7502701</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning of a Robusted Nearest Neighbor Classifier Using Multiple Training Data

Popis výsledku v původním jazyce

This paper deals with the application of face recognition in surveillance CCTV systems and effective usage of so called recognition clues. These clues are enrollment of multiple training face images and their usages in classifier training and real-time management of template database. A survey on classifiers from perspective of practical application is given resulting in the defense of nearest neighbor based classifiers. They are competitive with state of the art classifiers and are suitable for practical application. Template creation using multiple training face images and enhancement of NN-based classifier performance is achieved by novel approach. It consist of quantile interval method for template creation and robusted NNbased classifier using spatial templates with soft boundaries. We evaluate proposed recognition framework on highly representative IFaViD dataset. Proposed framework outperforms state of the art approaches.

Název v anglickém jazyce

Learning of a Robusted Nearest Neighbor Classifier Using Multiple Training Data

Popis výsledku anglicky

This paper deals with the application of face recognition in surveillance CCTV systems and effective usage of so called recognition clues. These clues are enrollment of multiple training face images and their usages in classifier training and real-time management of template database. A survey on classifiers from perspective of practical application is given resulting in the defense of nearest neighbor based classifiers. They are competitive with state of the art classifiers and are suitable for practical application. Template creation using multiple training face images and enhancement of NN-based classifier performance is achieved by novel approach. It consist of quantile interval method for template creation and robusted NNbased classifier using spatial templates with soft boundaries. We evaluate proposed recognition framework on highly representative IFaViD dataset. Proposed framework outperforms state of the art approaches.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/LO1401" target="_blank" >LO1401: Interdisciplinární výzkum bezdrátových technologií</a><br>

Návaznosti

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

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings The 23rd International Conference on Systems, Signals and Image Processing

ISBN

978-1-4673-9554-0

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

47-50

Název nakladatele

Neuveden

Místo vydání

Bratislava

Místo konání akce

Bratislava

Datum konání akce

23. 5. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000389830400006