Genetic Algorithm for Weight Optimization in Descriptor Based Face Recognition Methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F16%3A43929341" target="_blank" >RIV/49777513:23520/16:43929341 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5220/0005704403300336" target="_blank" >http://dx.doi.org/10.5220/0005704403300336</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0005704403300336" target="_blank" >10.5220/0005704403300336</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Genetic Algorithm for Weight Optimization in Descriptor Based Face Recognition Methods

Popis výsledku v původním jazyce

This paper presents a novel algorithm for weight optimization in descriptor based face recognition methods. We aim at the local texture features. Common concept in such methods is creating histograms of the operator values in rectangular image regions and concatenating them into one large vector called histogram sequence (HS). Usually the facial regions are given equal weight which does not correspond with the reality. We propose a novel method that optimizes the weights of the regions. The optimization method is based on a genetic algorithm (GA). We test the method together with the LBP and POEM descriptors. We evaluate our algorithms on two real-world corpora: Unconstrained facial images (UFI) database and FaceScrub database. The results show that the weighted methods outperform the non-weighted ones. The best achieved scores are 68.93% on the UFI database and 57.81% on the FaceScrub database.

Název v anglickém jazyce

Genetic Algorithm for Weight Optimization in Descriptor Based Face Recognition Methods

Popis výsledku anglicky

This paper presents a novel algorithm for weight optimization in descriptor based face recognition methods. We aim at the local texture features. Common concept in such methods is creating histograms of the operator values in rectangular image regions and concatenating them into one large vector called histogram sequence (HS). Usually the facial regions are given equal weight which does not correspond with the reality. We propose a novel method that optimizes the weights of the regions. The optimization method is based on a genetic algorithm (GA). We test the method together with the LBP and POEM descriptors. We evaluate our algorithms on two real-world corpora: Unconstrained facial images (UFI) database and FaceScrub database. The results show that the weighted methods outperform the non-weighted ones. The best achieved scores are 68.93% on the UFI database and 57.81% on the FaceScrub database.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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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

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 8th International Conference on Agents and Artificial Intelligence

ISBN

978-989-758-172-4

ISSN

—

e-ISSN

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Počet stran výsledku

7

Strana od-do

330-336

Název nakladatele

SciTePress

Místo vydání

Setúbal

Místo konání akce

Řím

Datum konání akce

24. 2. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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