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Multilayer Architectures for Facial Action Unit Recognition
2012
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
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Index Terms-Action unit recognition, facial expression recognition, Gabor energy filters (GEFs), local binary patterns (LBPs). ...
In this paper, we present a thorough empirical analysis of the performance of single-layer and dual-layer texture-based approaches for action unit recognition. ...
These elementary expressions, known as action units (AUs) and action descriptors (ADs), can be seen as the "phonemes" of facial expressions: words are temporal combinations of phonemes. ...
doi:10.1109/tsmcb.2012.2195170
pmid:22588611
fatcat:frpm2sy27bgttibvqiktosnsma
Facial Action Unit Detection with Multilayer Fused Multi-Task and Multi-Label Deep Learning Network
2019
KSII Transactions on Internet and Information Systems
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Facial action units (AUs) have recently drawn increased attention because they can be used to recognize facial expressions. ...
In this paper we propose a method for multi-view facial AU detection using a fused multilayer, multi-task, and multi-label deep learning network. ...
In a similar way, facial action unit detection could also be used for security or assisted medical care. ...
doi:10.3837/tiis.2019.11.015
fatcat:qrbozs5qhzhf3kxj3amtci6vfq
Facial Expression Recognition Using Convolutional Neural Network
2022
International Journal for Research in Applied Science and Engineering Technology
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An algorithm that performs detection, extraction, and evaluation of these facial expressions will allow for automatic recognition of human emotion in images and videos. ...
Keywords: Facial Expression Recognition, Convolutional Neural Network, Deep Learning. ...
Facial Action Coding System (FACS) was used to return special coefficients called Action Units (AU). There are 6 Action Units. These Action Units (AU) represent different region of face. ...
doi:10.22214/ijraset.2022.42439
fatcat:ni74zy6zxrcl5omh2l2c4eh5k4
Latent semantic analysis of facial action codes for automatic facial expression recognition
2004
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval - MIR '04
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For supervised training of automatic facial expression recognition systems, adequate ground truth labels that describe relevant facial expression categories are necessary. ...
Our approach exploits the fact that FACS codes can be seen as documents containing terms -the action units (AUs) present in the codes-and so text modeling methods that capture co-occurrence information ...
As a discrete representation, FACS codes feature a great number of different action unit combinations in order to allow for a comprehensive description of facial actions. ...
doi:10.1145/1026711.1026742
dblp:conf/mir/FaselMG04
fatcat:duaq7quwkzbvdhucwzwvmnbdxu
DeXpression: Deep Convolutional Neural Network for Expression Recognition
[article]
2016
arXiv
pre-print
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We propose a convolutional neural network (CNN) architecture for facial expression recognition. ...
For the MMI dataset, currently the best accuracy for emotion recognition is 93.33%. ...
ACKNOWLEDGMENTS We would like to thank the Affect Analysis Group of the University of Pittsburgh for providing the Extended CohnKanade database, and Prof. Pantic and Dr. Valstar for the MMI data-base. ...
arXiv:1509.05371v2
fatcat:utkzo65wgffi3ljwe2ixs27mje
Facial Expression Recognition Using 3D Facial Feature Distances
[chapter]
2008
Affective Computing
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Facial Action Coding System (FACS) was developed by Ekman and Friesen to code facial expressions in which the movements on the face are described by action units. ...
Basic architecture of facial expression recognition system Facial expression recognition includes both measurement of facial motion and recognition of expression. ...
doi:10.5772/6189
fatcat:2swf64gnifcnbootpojyk57eta
Deep learning the dynamic appearance and shape of facial action units
2016
2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
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In this work, we present a novel approach to Facial Action Unit detection using a combination of Convolutional and Bi-directional Long Short-Term Memory Neural Networks (CNN-BLSTM), which jointly learns ...
Spontaneous facial expression recognition under uncontrolled conditions is a hard task. ...
Dynamic encoding Temporal information can provide vital features for recognition of any facial action unit [24, 1] . ...
doi:10.1109/wacv.2016.7477625
dblp:conf/wacv/JaiswalV16
fatcat:r2ikxy235reohgd2ldklxm3tua
Facial Affect "In-the-Wild": A Survey and a New Database
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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facial behaviour and (b) detection of the activated facial muscles (i.e., facial action unit detection), to the best of our knowledge, well-established in-the-wild databases and benchmarks do not exist ...
Facial Affect "in-thewild": A survey and a new database. Abstract Well-established databases and benchmarks have been developed in the past 20 years for automatic facial behaviour analysis. ...
The last module is constructed by a multilayer RBM to learn hierarchical features, which are then concatenated for expression recognition. ...
doi:10.1109/cvprw.2016.186
dblp:conf/cvpr/ZafeiriouPKNZ16
fatcat:vabfqasierfv3euaw42wr6uiki
A Review on Deep Learning Algorithms for Speech and Facial Emotion Recognition
2016
APTIKOM Journal on Computer Science and Information Technologies
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Deep Learning technique has obtained remarkable success in the field of face recognition with 97.5% accuracy. Facial Electromyogram (FEMG) signals are used to detect the different emotions of humans. ...
Action Units
Convolutional
and Bi-
directional
Long Short-
Term Memory
Neural
Networks
(CNN-
BLSTM)
i) Learns the dynamic
appearance and shape of
facial regions for Action
Unit detection. ...
In March 2016, Shashank Jaiswal et al [21] , proposed a dynamic appearance for the shape of facial action unit using the convolutional and bi-directional long short-term memory neural networks (CNN-BLSTM ...
doi:10.11591/aptikom.j.csit.118
fatcat:gerpzx54qrgrtf3pqnnzgjywim
A Review on Deep Learning Algorithms for Speech and Facial Emotion Recognition
2020
APTIKOM Journal on Computer Science and Information Technologies
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Facial Electromyogram (FEMG) signals are used to detect the different emotionsof humans. ...
Action Units
Convolutional
and Bi-
directional
Long Short-
Term Memory
Neural
Networks
(CNN-
BLSTM)
i) Learns the dynamic
appearance and shape of
facial regions for Action
Unit detection. ...
In March 2016, Shashank Jaiswal et al [21] , proposed a dynamic appearance for the shape of facial action unit using the convolutional and bi-directional long short-term memory neural networks (CNN-BLSTM ...
doi:10.34306/csit.v1i3.55
fatcat:l2tska7j5ferna4wupt3f2jcp4
Elements for a Neural Theory of the Processing of Dynamic Faces
[chapter]
2010
Dynamic Faces
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Face recognition has been a central topic in computer vision for at least two decades and progress in recent years has been significant. ...
Computational Models for the Processing of Faces and Bodies The following section reviews work in computer vision as well as neural and psychological models for the recognition of static faces. ...
of approaches for the recognition of facial expressions and dynamic facial stimuli. ...
doi:10.7551/mitpress/9780262014533.003.0014
fatcat:yfgb4d5om5fkdlvofzfg3vfbo4
Simulation Study and At Home Diagnostic Tool for Early Detection of Parkinsons Disease
[article]
2021
arXiv
pre-print
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In addition, this project generates image datasets by simulating faces or action unit sets for both Parkinsons patients and non-affected individuals through coding. ...
This study aims to develop a diagnostic tool for Parkinsons disease utilizing the Facial Action Coding System, a comprehensive system describing all facially discernible movement. ...
Action Unit Description Facial muscle (Ekman & Friesen, 1978) Facial Expressions Action Units Disgust (AU4+AU7+AU9) Happy (AU1+AU6+AU12) Surprise (AU1+AU2+AU4)
Technology Used This analysis was conducted ...
arXiv:2111.12086v1
fatcat:5o2ioefwtvhr7jp4p2spigsgwe
Human Face Expressions from Images - 2D Face Geometry and 3D Face Local Motion versus Deep Neural Features
[article]
2019
arXiv
pre-print
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Several computer algorithms for recognition of visible human emotions are compared at the web camera scenario using CNN/MMOD face detector. ...
The recognition refers to four face expressions: smile, surprise, anger, and neutral. ...
There is great amount of databases for facial expression recognition from image as well as sophisticated recognition methods used in the wild. ...
arXiv:1901.11179v1
fatcat:un2qks6pp5cnplyluzxytcnyei
Multilayer GMDH-neuro-fuzzy network based on extended neo-fuzzy neurons and its application in online facial expression recognition
2020
Sistemnì Doslìdženâ ta Informacìjnì Tehnologìï
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The effectiveness of the proposed system is confirmed for the human emotions recognition. ...
This paper proposes a multilayer GMDH-neurofuzzy network based on extended neo-fuzzy neurons. ...
Under the emotions influence, the facial muscles reduction leads to the displacement of feature points and this movement can serve as an indicator of basic facial actions. ...
doi:10.20535/srit.2308-8893.2020.3.05
fatcat:gabhgu6pnfhixj6niptldfxknq
Deep Learning Techniques for Face Recognition: A Review
2019
International Journal for Research in Applied Science and Engineering Technology
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This paper provides an overview of deep learning techniques and its usage for face recognition. ...
The challenge of face recognition task is to extract the features accurately. Deep learning techniques are becoming popular as it is able to handle large datasets. ...
Deep learning is a solution for variety of computer vision problems, such as object detection, motion tracking , action recognition , human pose estimation , and semantic segmentation. ...
doi:10.22214/ijraset.2019.6271
fatcat:ng6adarrwvbenlsphuffeovyzm
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