Complement component face space for 3D face recognition from range images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50017289" target="_blank" >RIV/62690094:18450/21:50017289 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s10489-020-02012-8" target="_blank" >https://link.springer.com/article/10.1007/s10489-020-02012-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10489-020-02012-8" target="_blank" >10.1007/s10489-020-02012-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Complement component face space for 3D face recognition from range images

Popis výsledku v původním jazyce

This paper proposes a mathematical model for decomposing a range face image into four basic components (named ‘complement components’) in conjunction with a simple approach for data-level fusion to generate thirty-six additional hybrid components. These forty component faces composing a new face image space called the ‘complement component face space.’ The main challenge of this work was to extract relevant features from the vast face space. Features are extracted from the four basic components and four selected hybrid components using singular value decomposition. To introduce diversity, the extracted feature vectors are fused by applying the crossover operation of the genetic algorithm using a Hamming distance-based fitness measure. Particle swarm optimization-based feature selection is employed on the fused features to discard redundant feature values and to maximize the face recognition performance. The recognition performances of the proposed feature set with a support vector machine-based classifier on three accessible and well-known 3D face databases, namely, Frav3D, Bosphorus, and Texas3D, show significant improvements over those achieved by state-of-the-art methods. This work also studies the feasibility of utilizing the component images in the complement component face space for data augmentation in convolutional neural network (CNN)-based frameworks. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Název v anglickém jazyce

Complement component face space for 3D face recognition from range images

Popis výsledku anglicky

This paper proposes a mathematical model for decomposing a range face image into four basic components (named ‘complement components’) in conjunction with a simple approach for data-level fusion to generate thirty-six additional hybrid components. These forty component faces composing a new face image space called the ‘complement component face space.’ The main challenge of this work was to extract relevant features from the vast face space. Features are extracted from the four basic components and four selected hybrid components using singular value decomposition. To introduce diversity, the extracted feature vectors are fused by applying the crossover operation of the genetic algorithm using a Hamming distance-based fitness measure. Particle swarm optimization-based feature selection is employed on the fused features to discard redundant feature values and to maximize the face recognition performance. The recognition performances of the proposed feature set with a support vector machine-based classifier on three accessible and well-known 3D face databases, namely, Frav3D, Bosphorus, and Texas3D, show significant improvements over those achieved by state-of-the-art methods. This work also studies the feasibility of utilizing the component images in the complement component face space for data augmentation in convolutional neural network (CNN)-based frameworks. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

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

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Applied Intelligence

ISSN

0924-669X

e-ISSN

—

Svazek periodika

51

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

18

Strana od-do

2500-2517

Kód UT WoS článku

000642226300002

EID výsledku v databázi Scopus

2-s2.0-85095615560