Fused Geometry Augmented Images for Analyzing Textured Mesh

Bilal Taha, Munawar Hayat, Stefano Berretti, Naoufel Werghi

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

Abstract

In this paper, we propose a multi-modal mesh surface representation by fusing texture and geometric data. Our approach defines an inverse mapping between different geometric descriptors computed on the mesh surface, and the corresponding 2D texture image of the mesh, allowing the construction of fused geometrically augmented images. This new fused modality enables us to learn feature representations from 3D data in a highly efficient manner by employing standard convolutional neural networks in a transfer-learning mode. In contrast to existing methods, the proposed approach is both computationally and memory efficient, preserves intrinsic geometric information and learns highly discriminative feature representations by effectively fusing shape and texture information at the data level. The efficacy is demonstrated on the task of facial expression classification, showing competitive performance with state-of-the-art methods.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
EditorsMohammed Al Mualla, Moncef Gabbouj
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages2651-2655
Number of pages5
ISBN (Electronic)9781728163956
ISBN (Print)9781728163963
DOIs
Publication statusPublished - Oct 2020
Externally publishedYes
Event2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates
Duration: 25 Sep 202028 Sep 2020

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2020-October
ISSN (Print)1522-4880

Conference

Conference2020 IEEE International Conference on Image Processing, ICIP 2020
Country/TerritoryUnited Arab Emirates
CityVirtual, Abu Dhabi
Period25/09/2028/09/20

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