Title
Coupled Dictionary Learning for the Detail-Enhanced Synthesis of 3-D Facial Expressions.
Abstract
The desire to reconstruct 3-D face models with expressions from 2-D face images fosters increasing interest in addressing the problem of face modeling. This task is important and challenging in the field of computer animation. Facial contours and wrinkles are essential to generate a face with a certain expression; however, these details are generally ignored or are not seriously considered in previous studies on face model reconstruction. Thus, we employ coupled radius basis function networks to derive an intermediate 3-D face model from a single 2-D face image. To optimize the 3-D face model further through landmarks, a coupled dictionary that is related to 3-D face models and their corresponding 3-D landmarks is learned from the given training set through local coordinate coding. Another coupled dictionary is then constructed to bridge the 2-D and 3-D landmarks for the transfer of vertices on the face model. As a result, the final 3-D face can be generated with the appropriate expression. In the testing phase, the 2-D input faces are converted into 3-D models that display different expressions. Experimental results indicate that the proposed approach to facial expression synthesis can obtain model details more effectively than previous methods can.
Year
DOI
Venue
2016
10.1109/TCYB.2015.2417211
IEEE transactions on cybernetics
Keywords
DocType
Volume
coupled dictionary,expression synthesis,landmark,local coordinate coding (lcc)
Journal
PP
Issue
ISSN
Citations 
99
2168-2275
5
PageRank 
References 
Authors
0.38
26
4
Name
Order
Citations
PageRank
Haoran Liang162.09
Ronghua Liang237642.60
Mingli Song3164698.10
Xiaofei He49139386.38