3D Face Recognition Using a Fusion of PCA and ICA Convolution Descriptors

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F22%3A50019026" target="_blank" >RIV/62690094:18450/22:50019026 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s11063-022-10761-5" target="_blank" >https://link.springer.com/article/10.1007/s11063-022-10761-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11063-022-10761-5" target="_blank" >10.1007/s11063-022-10761-5</a>

Result language

angličtina

Original language name

3D Face Recognition Using a Fusion of PCA and ICA Convolution Descriptors

Original language description

This paper introduces a hybrid filter bank-based convolutional network to develop a 3D face recognition system in different orientations. The filter banks approach has been mainly used for feature representation. The hybridization in filter banks is primarily generated by a fusion of principal component analysis (PCA) and independent component analysis (ICA) filters. Currently, the deep convolutional neural network (DCNN) has taken a significant step for improving the classification compared to other learning, though the feature learning mechanism of DCNN is not definite. We have used the cascaded linear convolutional network for 3D face classification using a composite filter-based network named PICANet. The networks consist of different layers: convolutional layer, nonlinear processing layer, pooling layer, and classification layer. The main advantage of these networks over DCNN is that the network structure is simple and computationally efficient. We have tested the proposed system on three accessible 3D face databases: Frav3D, GavabDB, and Casia3D. Considering different faces in Frav3D, GavabDB, and Casia3D, the system acquired 96.93%, 87.7%, and 89.21% recognition rates using the proposed hybrid network. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Czech name

—

Czech description

—

Type

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

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Name of the periodical

Neural Processing Letters

ISSN

1370-4621

e-ISSN

1573-773X

Volume of the periodical

54

Issue of the periodical within the volume

4

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

21

Pages from-to

3507-3527

UT code for WoS article

000761842400003

EID of the result in the Scopus database

2-s2.0-85125285049