English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we present a novel approach to automatic 3D Facial ExpressionRecognition (FER) based on deep representation of facial 3D geometric and 2Dphotometric attributes. A 3D face is firstly represented by its geometric andphotometric attributes, including the geometry map, normal maps, normalizedcurvature map and texture map. These maps are then fed into a pre-trained deepconvolutional neural network to generate the deep representation. Then thefacial expression prediction is simplyachieved by training linear SVMs over thedeep representation for different maps and fusing these SVM scores. Thevisualizations show that the deep representation provides a complete and highlydiscriminative coding scheme for 3D faces. Comprehensive experiments on theBU-3DFE database demonstrate that the proposed deep representation canoutperform the widely used hand-crafted descriptors (i.e., LBP, SIFT, HOG,Gabor) and the state-of-art approaches under the same experimental protocols.

Deep Representation of Facial Geometric and Photometric Attributes for Automatic 3D Facial Expression Recognition