Visual facial expression modeling and early predicting from 3D data via subtle feature enhancing

This work investigates a new challenging problem: how to exactly recognize facial expression captured by a high-frame rate 3D sensing as early as possible, while most works generally focus on improving the recognition rate of 2D facial expression recognition. The recognition of subtle facial express...

Full description

Saved in:
Bibliographic Details
Published in:Multimedia tools and applications 2016-10, Vol.75 (20), p.12563-12580
Main Authors: Su, Lumei, Lu, Feng
Format: Article
Language:English
Subjects:
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Discriminant analysis
Face recognition
Facial
Human-computer interaction
Hypotheses
Mathematical models
Methods
Modelling
Motion capture
Multimedia Information Systems
Noise
Principal components analysis
Researchers
Special Purpose and Application-Based Systems
Three dimensional models
Three dimensional motion
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This work investigates a new challenging problem: how to exactly recognize facial expression captured by a high-frame rate 3D sensing as early as possible, while most works generally focus on improving the recognition rate of 2D facial expression recognition. The recognition of subtle facial expressions in their early stage is unfortunately very sensitive to noise that cannot be ignored due to their low intensity. To overcome this problem, two novel feature enhancement methods, namely, adaptive wavelet spectral subtraction method and SVM-based linear discriminant analysis, are proposed to refine subtle features of facial expressions by employing an estimated noise model or not. Experiments on a custom-made dataset built using a high-speed 3D motion capture system corroborated that the two proposed methods outperform other feature refinement methods by enhancing the discriminability of subtle facial expression features and consequently make correct recognitions earlier.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-014-2347-x