Accurate Facial Parts Localization and Deep Learning for 3D Facial
Expression Recognition
release_32ogfiwjijfq3mvxm46r2phdyq
by
Asim Jan, Huaxiong Ding, Hongying Meng, Liming Chen, Huibin Li
2018
Abstract
Meaningful facial parts can convey key cues for both facial action unit
detection and expression prediction. Textured 3D face scan can provide both
detailed 3D geometric shape and 2D texture appearance cues of the face which
are beneficial for Facial Expression Recognition (FER). However, accurate
facial parts extraction as well as their fusion are challenging tasks. In this
paper, a novel system for 3D FER is designed based on accurate facial parts
extraction and deep feature fusion of facial parts. In particular, each
textured 3D face scan is firstly represented as a 2D texture map and a depth
map with one-to-one dense correspondence. Then, the facial parts of both
texture map and depth map are extracted using a novel 4-stage process consists
of facial landmark localization, facial rotation correction, facial resizing,
facial parts bounding box extraction and post-processing procedures. Finally,
deep fusion Convolutional Neural Networks (CNNs) features of all facial parts
are learned from both texture maps and depth maps, respectively and nonlinear
SVMs are used for expression prediction. Experiments are conducted on the
BU-3DFE database, demonstrating the effectiveness of combing different facial
parts, texture and depth cues and reporting the state-of-the-art results in
comparison with all existing methods under the same setting.
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