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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

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

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

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

Name of the periodical

Applied Intelligence

ISSN

0924-669X

e-ISSN

—

Volume of the periodical

51

Issue of the periodical within the volume

4

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

18

Pages from-to

2500-2517

UT code for WoS article

000642226300002

EID of the result in the Scopus database

2-s2.0-85095615560