Pose, Expression, Illumination Invariant 3D Face Recognition Based on Transfer Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F24%3A50021805" target="_blank" >RIV/62690094:18450/24:50021805 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-981-97-6489-1_26" target="_blank" >http://dx.doi.org/10.1007/978-981-97-6489-1_26</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-97-6489-1_26" target="_blank" >10.1007/978-981-97-6489-1_26</a>

Result language

angličtina

Original language name

Pose, Expression, Illumination Invariant 3D Face Recognition Based on Transfer Learning

Original language description

Since the last decade, 3D face recognition is much more popular than 2D face recognition. The face recognition problem related to pose variation is easily solved by 3D face recognition. In this work, we have introduced a 3D face recognition system using a transfer learning-based approach. Deep learning is one of the most popular techniques in classification or recognition. The convolution neural network (CNN) in deep learning is mainly used for image-based recognition. However, a heavy dataset and high configuration machine are basic needs to develop a deep learning-based system. Due to the limitations of 3D databases consisting of a vast number of data, we have focused on a transfer learning-based approach on five different pre-trained network models such as ResNet50V2, InceptionV3, InceptionResNetV2, DenseNet201, and Xception. This approach transfers knowledge from a similar task to a new task for improving performance. Before feature extraction using the transfer learning approach, a binarization-based cropping technique was applied to a 3D depth image to remove outliers and extract the cropped face portion. The proposed 3D face recognition system considered two popular 3D face databases, Frav3 and Casia3D, for experimental analysis. The accuracy, recall, precision, and F1-score are calculated for all the pre-trained models for two individual databases. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Czech name

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Czech description

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Type

D - Article in proceedings

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

2024

Confidentiality

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

Article name in the collection

Lecture Notes in Networks and Systems

ISBN

978-981-9764-88-4

ISSN

2367-3370

e-ISSN

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Number of pages

11

Pages from-to

365-375

Publisher name

Springer Science and Business Media Deutschland GmbH

Place of publication

Singapore

Event location

Aizawl

Event date

Dec 15, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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