2112 Affect Display Effects of Anger by Virtual Facial Image Synthesis with Facial Color Enhancement and Expression

2006 ◽  
Vol 2006.16 (0) ◽  
pp. 146-149
Author(s):  
Takashi YAMADA ◽  
Tomio WATANABE
Keyword(s):  
Image Synthesis ◽  
Facial Image ◽  
Color Enhancement

scholarly journals Dual Semi-Supervised Learning for Facial Action Unit Recognition

2019 ◽  
Vol 33 ◽  
pp. 8827-8834 ◽  
Author(s):  
Guozhu Peng ◽  
Shangfei Wang
Keyword(s):  
Auxiliary Information ◽  
Image Synthesis ◽  
Training Data ◽  
Facial Image ◽  
Generative Adversarial Network ◽  
Informative Feedback ◽  
Action Unit ◽  
Facial Action ◽  
The Face ◽  
Facial Images

Current works on facial action unit (AU) recognition typically require fully AU-labeled training samples. To reduce the reliance on time-consuming manual AU annotations, we propose a novel semi-supervised AU recognition method leveraging two kinds of readily available auxiliary information. The method leverages the dependencies between AUs and expressions as well as the dependencies among AUs, which are caused by facial anatomy and therefore embedded in all facial images, independent on their AU annotation status. The other auxiliary information is facial image synthesis given AUs, the dual task of AU recognition from facial images, and therefore has intrinsic probabilistic connections with AU recognition, regardless of AU annotations. Specifically, we propose a dual semi-supervised generative adversarial network for AU recognition from partially AU-labeled and fully expressionlabeled facial images. The proposed network consists of an AU classifier C, an image generator G, and a discriminator D. In addition to minimize the supervised losses of the AU classifier and the face generator for labeled training data, we explore the probabilistic duality between the tasks using adversary learning to force the convergence of the face-AU-expression tuples generated from the AU classifier and the face generator, and the ground-truth distribution in labeled data for all training data. This joint distribution also includes the inherent AU dependencies. Furthermore, we reconstruct the facial image using the output of the AU classifier as the input of the face generator, and create AU labels by feeding the output of the face generator to the AU classifier. We minimize reconstruction losses for all training data, thus exploiting the informative feedback provided by the dual tasks. Within-database and cross-database experiments on three benchmark databases demonstrate the superiority of our method in both AU recognition and face synthesis compared to state-of-the-art works.

A Texture Mapping Approach to 3-D Facial Image Synthesis

1988 ◽  
Vol 7 (2) ◽  
pp. 129-134 ◽  
Author(s):  
John F.S. Yau ◽  
Neil D. Duffy
Keyword(s):  
Texture Mapping ◽  
Image Synthesis ◽  
Facial Image ◽  
Mapping Approach

A realtime model-based facial image synthesis based on multiprocessor network

1991 ◽  
Vol 22 (13) ◽  
pp. 59-69 ◽  
Author(s):  
Shigeo Morishima ◽  
Seiji Kobayashi ◽  
Hiroshi Harashima
Keyword(s):  
Image Synthesis ◽  
Facial Image ◽  
Model Based

Facial image synthesis using skin texture recording

1990 ◽  
Vol 6 (6) ◽  
pp. 337-343 ◽  
Author(s):  
M. Nahas ◽  
H. Huitric ◽  
M. Rioux ◽  
J. Domey
Keyword(s):  
Image Synthesis ◽  
Facial Image ◽  
Skin Texture
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