scholarly journals Feature Ranking Ensembles for Facial Action Unit Classification

2008 ◽  
pp. 267-279 ◽  
Author(s):  
Terry Windeatt ◽  
Kaushala Dias
Keyword(s):  
Feature Ranking ◽  
Action Unit ◽  
Facial Action

Tunneled Latent Variables Method for Facial Action Unit Tracking

2009 ◽  
Vol 35 (2) ◽  
pp. 198-201 ◽  
Author(s):  
Lei WANG ◽  
Bei-Ji ZOU ◽  
Xiao-Ning PENG
Keyword(s):  
Latent Variables ◽  
Action Unit ◽  
Facial Action

Multi-view facial action unit detection via DenseNets and CapsNets

2021 ◽  
Author(s):  
Dakai Ren ◽  
Xiangmin Wen ◽  
Jiazhong Chen ◽  
Yu Han ◽  
Shiqi Zhang
Keyword(s):  
Action Unit ◽  
Facial Action ◽  
Action Unit Detection

scholarly journals Assessing Automated Facial Action Unit Detection Systems for Analyzing Cross-Domain Facial Expression Databases

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4222
Author(s):  
Shushi Namba ◽  
Wataru Sato ◽  
Masaki Osumi ◽  
Koh Shimokawa
Keyword(s):  
Affective Computing ◽  
Detection System ◽  
Characteristic Curve ◽  
Unmet Need ◽  
Systematic Evaluation ◽  
Dynamic Mode ◽  
Coding System ◽  
Static Mode ◽  
Action Unit ◽  
Facial Action

In the field of affective computing, achieving accurate automatic detection of facial movements is an important issue, and great progress has already been made. However, a systematic evaluation of systems that now have access to the dynamic facial database remains an unmet need. This study compared the performance of three systems (FaceReader, OpenFace, AFARtoolbox) that detect each facial movement corresponding to an action unit (AU) derived from the Facial Action Coding System. All machines could detect the presence of AUs from the dynamic facial database at a level above chance. Moreover, OpenFace and AFAR provided higher area under the receiver operating characteristic curve values compared to FaceReader. In addition, several confusion biases of facial components (e.g., AU12 and AU14) were observed to be related to each automated AU detection system and the static mode was superior to dynamic mode for analyzing the posed facial database. These findings demonstrate the features of prediction patterns for each system and provide guidance for research on facial expressions.

scholarly journals Semantic Relationships Guided Representation Learning for Facial Action Unit Recognition

2019 ◽  
Vol 33 ◽  
pp. 8594-8601 ◽  
Author(s):  
Guanbin Li ◽  
Xin Zhu ◽  
Yirui Zeng ◽  
Qing Wang ◽  
Liang Lin
Keyword(s):  
Neural Network ◽  
Facial Expressions ◽  
Mutual Exclusion ◽  
Representation Learning ◽  
Feature Representation ◽  
Semantic Relationship ◽  
Action Unit ◽  
Facial Action ◽  
Feature Representations ◽  
Regional Feature

Facial action unit (AU) recognition is a crucial task for facial expressions analysis and has attracted extensive attention in the field of artificial intelligence and computer vision. Existing works have either focused on designing or learning complex regional feature representations, or delved into various types of AU relationship modeling. Albeit with varying degrees of progress, it is still arduous for existing methods to handle complex situations. In this paper, we investigate how to integrate the semantic relationship propagation between AUs in a deep neural network framework to enhance the feature representation of facial regions, and propose an AU semantic relationship embedded representation learning (SRERL) framework. Specifically, by analyzing the symbiosis and mutual exclusion of AUs in various facial expressions, we organize the facial AUs in the form of structured knowledge-graph and integrate a Gated Graph Neural Network (GGNN) in a multi-scale CNN framework to propagate node information through the graph for generating enhanced AU representation. As the learned feature involves both the appearance characteristics and the AU relationship reasoning, the proposed model is more robust and can cope with more challenging cases, e.g., illumination change and partial occlusion. Extensive experiments on the two public benchmarks demonstrate that our method outperforms the previous work and achieves state of the art performance.

scholarly journals EAC-Net: Deep Nets with Enhancing and Cropping for Facial Action Unit Detection

2018 ◽  
Vol 40 (11) ◽  
pp. 2583-2596 ◽  
Author(s):  
Wei Li ◽  
Farnaz Abtahi ◽  
Zhigang Zhu ◽  
Lijun Yin
Keyword(s):  
Action Unit ◽  
Facial Action ◽  
Action Unit Detection

Exploiting Facial Action Unit in Video for Recognizing Depression using Metaheuristic and Neural Networks

2021 ◽  
Author(s):  
Habibullah Akbar ◽  
Sintia Dewi ◽  
Yuli Azmi Rozali ◽  
Lita Patricia Lunanta ◽  
Nizirwan Anwar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Action Unit ◽  
Facial Action

Facial Action Unit and Emotion Recognition with Head Pose Variations

2012 ◽  
pp. 383-394 ◽  
Author(s):  
Chadi Trad ◽  
Hazem Hajj ◽  
Wassim El-Hajj ◽  
Fatima Al-Jamil
Keyword(s):  
Emotion Recognition ◽  
Head Pose ◽  
Action Unit ◽  
Facial Action
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