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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

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

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Networks and Systems

ISBN

978-981-9764-88-4

ISSN

2367-3370

e-ISSN

—

Počet stran výsledku

11

Strana od-do

365-375

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Singapore

Místo konání akce

Aizawl

Datum konání akce

15. 12. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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