Improving Face Recognition Methods Based on POEM Features

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F20%3A43958230" target="_blank" >RIV/49777513:23520/20:43958230 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scitepress.org/Link.aspx?doi=10.5220/0008950305380545" target="_blank" >https://www.scitepress.org/Link.aspx?doi=10.5220/0008950305380545</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0008950305380545" target="_blank" >10.5220/0008950305380545</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving Face Recognition Methods Based on POEM Features

Popis výsledku v původním jazyce

The usual way how POEM descriptors are utilized consists in constructing features in rectangular non-overlapping regions covering the whole image. The features created in the regions are then concatenated into one long vector representing the face. We propose an enhancement of this method using automatic key-point identification strategies. In our approach, the image features are created in the detected key-points. We also employ a more complex matching procedure that compares the features individually. This method is efficient particularly when the number of training samples is small and therefore neural network based methods fail, because they do not have enough training data. The proposed approach is evaluated on three standard face corpora. The obtained results show that the combination of POEM features with the automatic point identification and a more sophisticated matching algorithm brings significant improvement over the baseline method.

Název v anglickém jazyce

Improving Face Recognition Methods Based on POEM Features

Popis výsledku anglicky

The usual way how POEM descriptors are utilized consists in constructing features in rectangular non-overlapping regions covering the whole image. The features created in the regions are then concatenated into one long vector representing the face. We propose an enhancement of this method using automatic key-point identification strategies. In our approach, the image features are created in the detected key-points. We also employ a more complex matching procedure that compares the features individually. This method is efficient particularly when the number of training samples is small and therefore neural network based methods fail, because they do not have enough training data. The proposed approach is evaluated on three standard face corpora. The obtained results show that the combination of POEM features with the automatic point identification and a more sophisticated matching algorithm brings significant improvement over the baseline method.

Druh

D - Stať ve sborníku

CEP obor

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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)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 12th International Conference on Agents and Artificial Intelligence

ISBN

978-989-758-395-7

ISSN

2184-433X

e-ISSN

—

Počet stran výsledku

8

Strana od-do

538-545

Název nakladatele

ScitePress

Místo vydání

Setúbal

Místo konání akce

Valletta, Malta

Datum konání akce

22. 2. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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