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behavioural sciences computing
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Cavallaro, A.[Andrea],
Learning Bases of Activity for Facial Expression Recognition,
IP(26), No. 4, April 2017, pp. 1965-1978.
IEEE DOI
1704
Computational modeling
BibRef
Sariyanidi, E.[Evangelos],
Gunes, H.[Hatice],
Cavallaro, A.[Andrea],
Robust Registration of Dynamic Facial Sequences,
IP(26), No. 4, April 2017, pp. 1708-1722.
IEEE DOI
1704
face recognition
BibRef
Chiranjeevi, P.,
Gopalakrishnan, V.,
Moogi, P.,
Neutral Face Classification Using Personalized Appearance Models for
Fast and Robust Emotion Detection,
IP(24), No. 9, September 2015, pp. 2701-2711.
IEEE DOI
1506
computer vision
BibRef
Rao, K.S.[K. Sreenivasa],
Koolagudi, S.G.[Shashidhar G.],
Recognition of emotions from video using acoustic and facial features,
SIViP(9), No. 5, July 2015, pp. 1029-1045.
WWW Link.
1506
BibRef
Teijeiro-Mosquera, L.,
Biel, J.I.,
Alba-Castro, J.L.,
Gatica-Perez, D.,
What Your Face Vlogs About: Expressions of Emotion and Big-Five
Traits Impressions in YouTube,
AffCom(6), No. 2, April 2015, pp. 193-205.
IEEE DOI
1507
emotion recognition
BibRef
Savran, A.,
Gur, R.,
Verma, R.,
Automatic Detection of Emotion Valence on Faces Using Consumer Depth
Cameras,
CDC4CV13(75-82)
IEEE DOI
1403
cameras
BibRef
Abadi, M.K.,
Subramanian, R.,
Kia, S.M.,
Avesani, P.,
Patras, I.,
Sebe, N.,
DECAF: MEG-Based Multimodal Database for Decoding Affective
Physiological Responses,
AffCom(6), No. 3, July 2015, pp. 209-222.
IEEE DOI
1509
Dataset, Affective Responses. Databases
BibRef
Zhang, L.[Li],
Mistry, K.[Kamlesh],
Jiang, M.[Ming],
Neoh, S.C.[Siew Chin],
Hossain, M.A.[Mohammed Alamgir],
Adaptive facial point detection and emotion recognition for a
humanoid robot,
CVIU(140), No. 1, 2015, pp. 93-114.
Elsevier DOI
1509
Facial point detection
BibRef
Sanchez-Mendoza, D.[David],
Masip, D.[David],
Lapedriza, A.[Agata],
Emotion recognition from mid-level features,
PRL(67, Part 1), No. 1, 2015, pp. 66-74.
Elsevier DOI
1511
Facial expression
BibRef
Ringeval, F.[Fabien],
Eyben, F.[Florian],
Kroupi, E.[Eleni],
Yuce, A.[Anil],
Thiran, J.P.[Jean-Philippe],
Ebrahimi, T.[Touradj],
Lalanne, D.[Denis],
Schuller, B.[Björn],
Prediction of asynchronous dimensional emotion ratings from
audiovisual and physiological data,
PRL(66), No. 1, 2015, pp. 22-30.
Elsevier DOI
1511
Context-learning long short-term memory recurrent neural networks
BibRef
Esposito, A.[Anna],
Esposito, A.M.[Antonietta M.],
Vogel, C.[Carl],
Needs and challenges in human computer interaction for processing
social emotional information,
PRL(66), No. 1, 2015, pp. 41-51.
Elsevier DOI
1511
Emotional expressions
BibRef
Provost, E.M.,
Shangguan, Y.,
Busso, C.,
UMEME: University of Michigan Emotional McGurk Effect Data Set,
AffCom(6), No. 4, October 2015, pp. 395-409.
IEEE DOI
1512
Dataset, Emotion Recognition. Emotion recognition
BibRef
Busso, C.,
Parthasarathy, S.,
Burmania, A.,
Abdel Wahab, M.,
Sadoughi, N.,
Provost, E.M.,
MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion
Perception,
AffCom(8), No. 1, January 2017, pp. 67-80.
IEEE DOI
1703
Affective computing
BibRef
Wang, S.,
Ji, Q.,
Video Affective Content Analysis:
A Survey of State-of-the-Art Methods,
AffCom(6), No. 4, October 2015, pp. 410-430.
IEEE DOI
1512
Survey, Affect Analsis. Content analysis
BibRef
Park, B.J.[Byoung-Jun],
Jang, E.H.[Eun-Hye],
Kim, K.H.[Kyong-Ho],
Kim, S.H.[Sang-Hyeob],
Analysis of Physiological Responses and Use of Fuzzy Information
Granulation-Based Neural Network for Recognition of Three Emotions,
ETRI(37), No. 6, December 2015, pp. 1231-1241.
DOI Link
1601
BibRef
Yoon, H.[Hyunjin],
Park, S.W.[Sang-Wook],
Lee, Y.[Yongkwi],
Han, M.[Mikyong],
Jang, J.H.[Jong-Hyun],
Improved Two-Phase Framework for Facial Emotion Recognition,
ETRI(37), No. 6, December 2015, pp. 1199-1210.
DOI Link
1601
BibRef
Truong, A.[Arthur],
Boujut, H.[Hugo],
Zaharia, T.[Titus],
Laban descriptors for gesture recognition and emotional analysis,
VC(32), No. 1, January 2016, pp. 83-98.
WWW Link.
1602
BibRef
Soleymani, M.,
Asghari-Esfeden, S.,
Fu, Y.,
Pantic, M.,
Analysis of EEG Signals and Facial Expressions for Continuous Emotion
Detection,
AffCom(7), No. 1, January 2016, pp. 17-28.
IEEE DOI
1603
Databases
BibRef
Meng, H.,
Bianchi-Berthouze, N.,
Deng, Y.,
Cheng, J.,
Cosmas, J.P.,
Time-Delay Neural Network for Continuous Emotional Dimension
Prediction From Facial Expression Sequences,
Cyber(46), No. 4, April 2016, pp. 916-929.
IEEE DOI
1604
Delays
BibRef
Georgakis, C.,
Panagakis, Y.[Yannis],
Pantic, M.[Maja],
Discriminant Incoherent Component Analysis,
IP(25), No. 5, May 2016, pp. 2021-2034.
IEEE DOI
1604
emotion recognition
BibRef
Huang, X.H.[Xiao-Hua],
Kortelainen, J.[Jukka],
Zhao, G.Y.[Guo-Ying],
Li, X.[Xiaobai],
Moilanen, A.[Antti],
Seppänen, T.[Tapio],
Pietikäinen, M.[Matti],
Multi-modal emotion analysis from facial expressions and
electroencephalogram,
CVIU(147), No. 1, 2016, pp. 114-124.
Elsevier DOI
1605
Multi-modal emotion recognition
BibRef
Gao, L.[Lei],
Qi, L.[Lin],
Guan, L.[Ling],
Online Behavioral Analysis with Application to Emotion State
Identification,
IEEE_Int_Sys(31), No. 5, September 2016, pp. 32-39.
IEEE DOI
1610
BibRef
Earlier:
Sparsity preserving multiple canonical correlation analysis with
visual emotion recognition to multi-feature fusion,
ICIP15(2710-2714)
IEEE DOI
1512
emotion recognition.
Analytical models
BibRef
Han, J.H.[Ji-Hyeong],
Choi, S.H.[Seung-Hwan],
Kim, J.H.[Jong-Hwan],
Interactive Human Intention Reading by Learning Hierarchical Behavior
Knowledge Networks for Human-Robot Interaction,
ETRI(38), No. 6, December 2016, pp. 1229-1239.
DOI Link
1612
BibRef
Gunes, H.[Hatice],
Hung, H.[Hayley],
Is automatic facial expression recognition of emotions coming to a
dead end? The rise of the new kids on the block,
IVC(55, Part 1), No. 1, 2016, pp. 6-8.
Elsevier DOI
1612
Opinion paper
BibRef
Zhao, S.,
Yao, H.,
Gao, Y.,
Ji, R.,
Ding, G.,
Continuous Probability Distribution Prediction of Image Emotions via
Multitask Shared Sparse Regression,
MultMed(19), No. 3, March 2017, pp. 632-645.
IEEE DOI
1702
Distance measurement
BibRef
Zhao, S.,
Yao, H.,
Gao, Y.,
Ding, G.,
Chua, T.,
Predicting Personalized Image Emotion Perceptions in Social Networks,
AffCom(9), No. 4, October 2018, pp. 526-540.
IEEE DOI
1812
Feature extraction, Social network services, Visualization,
Bridges, Context, Meteorology, Clouds, Personalized image emotion,
heypergraph learning
BibRef
Xia, R.,
Liu, Y.,
A Multi-Task Learning Framework for Emotion Recognition Using 2D
Continuous Space,
AffCom(8), No. 1, January 2017, pp. 3-14.
IEEE DOI
1703
Acoustics
BibRef
Dingli, A.[Alexiei],
Giordimaina, A.[Andreas],
Webcam-based detection of emotional states,
VC(33), No. 4, April 2017, pp. 459-469.
WWW Link.
1704
BibRef
Kruijff, E.[Ernst],
Marquardt, A.[Alexander],
Trepkowski, C.[Christina],
Schild, J.[Jonas],
Hinkenjann, A.[André],
Designed emotions: challenges and potential methodologies for improving
multisensory cues to enhance user engagement in immersive systems,
VC(33), No. 4, April 2017, pp. 471-488.
Springer DOI
1704
BibRef
Jiang, R.[Richard],
Ho, A.T.S.[Anthony T.S.],
Cheheb, I.[Ismahane],
Al-Maadeed, N.[Noor],
Al-Maadeed, S.[Somaya],
Bouridane, A.[Ahmed],
Emotion recognition from scrambled facial images via many graph
embedding,
PR(67), No. 1, 2017, pp. 245-251.
Elsevier DOI
1704
Facial expression
BibRef
Mistry, K.,
Zhang, L.,
Neoh, S.C.,
Lim, C.P.,
Fielding, B.,
A Micro-GA Embedded PSO Feature Selection Approach to Intelligent
Facial Emotion Recognition,
Cyber(47), No. 6, June 2017, pp. 1496-1509.
IEEE DOI
1706
Convergence, Emotion recognition, Face recognition,
Feature extraction, Genetic algorithms, Optimization,
Search problems, Ensemble classifier,
facial expression recognition, feature selection, particle swarm
optimization, (PSO)
BibRef
Wu, B.,
Jia, J.,
Yang, Y.,
Zhao, P.,
Tang, J.,
Tian, Q.,
Inferring Emotional Tags From Social Images With User Demographics,
MultMed(19), No. 7, July 2017, pp. 1670-1684.
IEEE DOI
1706
Computer science, Correlation, Feature extraction,
Learning systems, Social network services,
Support vector machines, Visualization, Emotion, image, user, demographics
BibRef
Zhalehpour, S.,
Onder, O.,
Akhtar, Z.,
Erdem, C.E.,
BAUM-1: A Spontaneous Audio-Visual Face Database of Affective and
Mental States,
AffCom(8), No. 3, July 2017, pp. 300-313.
IEEE DOI
1709
Affective computing, Cameras, Databases, Emotion recognition, Face,
Face recognition,
Facial expression recognition, affective computing,
audio-visual affective database,
dynamic facial expression database,
emotion recognition from speech, emotional corpora,
mental state recognition, spontaneous, expressions
BibRef
Kaya, H.[Heysem],
Gürpinar, F.[Furkan],
Salah, A.A.[Albert Ali],
Video-based emotion recognition in the wild using deep transfer
learning and score fusion,
IVC(65), No. 1, 2017, pp. 66-75.
Elsevier DOI
1709
EmotiW
BibRef
Rathee, N.[Neeru],
Ganotra, D.[Dinesh],
Discriminative analysis of lip features for emotion recognition,
IJCVR(7), No. 6, 2017, pp. 669-691.
DOI Link
1711
BibRef
Wei, Q.L.[Qing-Lan],
Sun, B.[Bo],
He, J.[Jun],
Yu, L.J.[Le-Jun],
BNU-LSVED 2.0: Spontaneous multimodal student affect database with
multi-dimensional labels,
SP:IC(59), No. 1, 2017, pp. 168-181.
Elsevier DOI
1711
Multimodal
BibRef
Turker, B.B.,
Yemez, Y.,
Sezgin, T.M.,
Erzin, E.,
Audio-Facial Laughter Detection in Naturalistic Dyadic Conversations,
AffCom(8), No. 4, October 2017, pp. 534-545.
IEEE DOI
1712
Bagging, Databases, Feature extraction, Hidden Markov models,
Magnetic heads,
naturalistic dyadic conversations
BibRef
Ferrari, C.,
Lisanti, G.,
Berretti, S.[Stefano],
del Bimbo, A.[Alberto],
A Dictionary Learning-Based 3D Morphable Shape Model,
MultMed(19), No. 12, December 2017, pp. 2666-2679.
IEEE DOI
1712
Face, Face recognition, Shape, Solid modeling,
Training.
emotion recognition
BibRef
Daoudi, M.[Mohamed],
Berretti, S.[Stefano],
Pala, P.[Pietro],
Delevoye, Y.[Yvonne],
del Bimbo, A.[Alberto],
Emotion Recognition by Body Movement Representation on the Manifold of
Symmetric Positive Definite Matrices,
CIAP17(I:550-560).
Springer DOI
1711
BibRef
Li, X.,
Rao, Y.,
Xie, H.,
Lau, R.Y.K.,
Yin, J.,
Wang, F.L.,
Bootstrapping Social Emotion Classification with Semantically Rich
Hybrid Neural Networks,
AffCom(8), No. 4, October 2017, pp. 428-442.
IEEE DOI
1712
Computational modeling, Education, Encoding, Feature extraction,
Neural networks, Semantics, Unsupervised learning,
transfer learning
BibRef
Hong, K.[Kan],
Liu, G.D.[Guo-Dong],
Chen, W.[Wentao],
Hong, S.[Sheng],
Classification of the emotional stress and physical stress using
signal magnification and canonical correlation analysis,
PR(77), 2018, pp. 140-149.
Elsevier DOI
1802
Emotional stress, Physical stress, Emotion classification
BibRef
Agarwal, S.[Swapna],
Santra, B.[Bikash],
Mukherjee, D.P.[Dipti Prasad],
Anubhav: recognizing emotions through facial expression,
VC(34), No. 2, February 2018, pp. 177-191.
WWW Link.
1802
BibRef
Seng, K.P.,
Ang, L.M.,
Ooi, C.S.,
A Combined Rule-Based Machine Learning Audio-Visual Emotion
Recognition Approach,
AffCom(9), No. 1, January 2018, pp. 3-13.
IEEE DOI
1804
audio signal processing, cepstral analysis, emotion recognition,
feature extraction, learning (artificial intelligence),
rule-based
BibRef
Fourati, N.,
Pelachaud, C.,
Perception of Emotions and Body Movement in the Emilya Database,
AffCom(9), No. 1, January 2018, pp. 90-101.
IEEE DOI
1804
BibRef
Earlier:
Multi-level classification of emotional body expression,
FG15(1-8)
IEEE DOI
1508
emotion recognition, visual databases, EMotional body expression,
Emilya database, body movement, emotion perception,
emotion perception
BibRef
Benitez-Garcia, G.[Gibran],
Nakamura, T.[Tomoaki],
Kaneko, M.[Masahide],
Multicultural Facial Expression Recognition Based on Differences of
Western-Caucasian and East-Asian Facial Expressions of Emotions,
IEICE(E101-D), No. 5, May 2018, pp. 1317-1324.
WWW Link.
1805
BibRef
Kalsum, T.[Tehmina],
Anwar, S.M.[Syed Muhammad],
Majid, M.[Muhammad],
Khan, B.[Bilal],
Ali, S.M.[Sahibzada Muhammad],
Emotion recognition from facial expressions using hybrid feature
descriptors,
IET-IPR(12), No. 6, June 2018, pp. 1004-1012.
DOI Link
1805
BibRef
Liu, W.F.[Wei-Feng],
Zhang, L.[Lianbo],
Tao, D.P.[Da-Peng],
Cheng, J.[Jun],
Reinforcement online learning for emotion prediction by using
physiological signals,
PRL(107), 2018, pp. 123-130.
Elsevier DOI
1805
Emotion prediction, Reinforcement learning, Online learning,
Biometric surveillance
BibRef
Xu, B.,
Fu, Y.,
Jiang, Y.G.,
Li, B.,
Sigal, L.,
Heterogeneous Knowledge Transfer in Video Emotion Recognition,
Attribution and Summarization,
AffCom(9), No. 2, April 2018, pp. 255-270.
IEEE DOI
1806
Emotion recognition, Feature extraction, Image recognition,
Knowledge transfer, Semantics, Training, Visualization,
zero-shot learning
BibRef
Seng, K.P.,
Ang, L.M.,
Video Analytics for Customer Emotion and Satisfaction at Contact
Centers,
HMS(48), No. 3, June 2018, pp. 266-278.
IEEE DOI
1805
Companies, Customer satisfaction, Emotion recognition, Speech,
Speech recognition, Visualization, Customer experience,
video analytics
BibRef
Yan, J.J.[Jing-Jie],
Wang, B.[Bei],
Liang, R.[Ruiyu],
A Novel Bimodal Emotion Database from Physiological Signals and Facial
Expression,
IEICE(E101-D), No. 7, July 2018, pp. 1976-1979.
WWW Link.
1807
Dataset, Emotions.
BibRef
Sreedharan, N.P.N.[Ninu Preetha Nirmala],
Ganesan, B.[Brammya],
Raveendran, R.[Ramya],
Sarala, P.[Praveena],
Dennis, B.[Binu],
Boothalingam, R.R.[R. Rajakumar],
Grey Wolf optimisation-based feature selection and classification for
facial emotion recognition,
IET-Bio(7), No. 5, September 2018, pp. 490-499.
DOI Link
1809
BibRef
Pons, G.,
Masip, D.,
Supervised Committee of Convolutional Neural Networks in Automated
Facial Expression Analysis,
AffCom(9), No. 3, July 2018, pp. 343-350.
IEEE DOI
1809
Emotion recognition, Training, Databases, Computer architecture,
Computer vision, Facial emotion recognition,
convolutional neural networks
BibRef
Schuller, D.,
Schuller, B.W.,
The Age of Artificial Emotional Intelligence,
Computer(51), No. 9, September 2018, pp. 38-46.
IEEE DOI
1810
Artificial intelligence, Emotion recognition, Machine learning,
Learning systems, Artificial Emotional Intelligence,
future of AI
BibRef
Zhang, H.,
Xu, M.,
Recognition of Emotions in User-Generated Videos With Kernelized
Features,
MultMed(20), No. 10, October 2018, pp. 2824-2835.
IEEE DOI
1810
discrete Fourier transforms, emotion recognition,
feature extraction, image classification, image representation,
kernel method
BibRef
Kim, H.,
Kim, Y.,
Kim, S.J.,
Lee, I.,
Building Emotional Machines: Recognizing Image Emotions Through Deep
Neural Networks,
MultMed(20), No. 11, November 2018, pp. 2980-2992.
IEEE DOI
1810
Feature extraction, Emotion recognition, Machine learning,
Predictive models, Databases, Task analysis, Psychology,
deep network
BibRef
Nguyen, D.[Dung],
Nguyen, K.[Kien],
Sridharan, S.[Sridha],
Dean, D.[David],
Fookes, C.[Clinton],
Deep spatio-temporal feature fusion with compact bilinear pooling for
multimodal emotion recognition,
CVIU(174), 2018, pp. 33-42.
Elsevier DOI
1812
BibRef
Nguyen, D.[Dung],
Nguyen, K.[Kien],
Sridharan, S.[Sridha],
Ghasemi, A.[Afsane],
Dean, D.[David],
Fookes, C.[Clinton],
Deep Spatio-Temporal Features for Multimodal Emotion Recognition,
WACV17(1215-1223)
IEEE DOI
1609
Convolution, Emotion recognition, Face, Feature extraction, Speech,
Speech recognition, Streaming, media
BibRef
Tsalamlal, M.Y.,
Amorim, M.,
Martin, J.,
Ammi, M.,
Combining Facial Expression and Touch for Perceiving Emotional
Valence,
AffCom(9), No. 4, October 2018, pp. 437-449.
IEEE DOI
1812
Face recognition, Visualization, Haptic interfaces,
Emotion recognition, Human computer interaction,
multimodality
BibRef
Lingenfelser, F.,
Wagner, J.,
Deng, J.,
Brueckner, R.,
Schuller, B.,
André, E.,
Asynchronous and Event-Based Fusion Systems for Affect Recognition on
Naturalistic Data in Comparison to Conventional Approaches,
AffCom(9), No. 4, October 2018, pp. 410-423.
IEEE DOI
1812
Hidden Markov models, Emotion recognition,
Recurrent neural networks, Visualization, Heuristic algorithms,
deep learning
BibRef
Jain, N.[Neha],
Kumar, S.[Shishir],
Kumar, A.[Amit],
Shamsolmoali, P.[Pourya],
Zareapoor, M.[Masoumeh],
Hybrid deep neural networks for face emotion recognition,
PRL(115), 2018, pp. 101-106.
Elsevier DOI
1812
Emotion recognition, Deep learning, Recurrent neural networks,
Convolutional Neural Networks, Hybrid CNN-RNN
BibRef
Gong, X.M.[Xiao-Min],
Shen, H.[Huizhang],
Han, X.[Xiao],
Influence of emotion and cognitive demand on frame effect in crisis
decision-making,
PRL(116), 2018, pp. 262-265.
Elsevier DOI
1812
Crisis decision making, Frame effect, Emotion and cognition,
Information dissemination
BibRef
Zheng, W.,
Liu, W.,
Lu, Y.,
Lu, B.,
Cichocki, A.,
EmotionMeter: A Multimodal Framework for Recognizing Human Emotions,
Cyber(49), No. 3, March 2019, pp. 1110-1122.
IEEE DOI
1902
Electroencephalography, Emotion recognition, Electrodes,
Feature extraction, Human computer interaction,
multimodal deep neural networks
BibRef
Poria, S.,
Majumder, N.,
Hazarika, D.,
Cambria, E.,
Gelbukh, A.,
Hussain, A.,
Multimodal Sentiment Analysis: Addressing Key Issues and Setting Up
the Baselines,
IEEE_Int_Sys(33), No. 6, November 2018, pp. 17-25.
IEEE DOI
1902
Sentiment analysis, Feature extraction, Visualization,
Emotion recognition, Affective computing,
Intelligent systems
BibRef
Noroozi, F.,
Marjanovic, M.,
Njegus, A.,
Escalera, S.,
Anbarjafari, G.,
Audio-Visual Emotion Recognition in Video Clips,
AffCom(10), No. 1, January 2019, pp. 60-75.
IEEE DOI
1903
Emotion recognition, Visualization, Feature extraction, Databases,
Face, Neural networks, Mel frequency cepstral coefficient,
convolutional neural networks
BibRef
Zhang, B.,
Provost, E.M.,
Essl, G.,
Cross-Corpus Acoustic Emotion Recognition with Multi-Task Learning:
Seeking Common Ground While Preserving Differences,
AffCom(10), No. 1, January 2019, pp. 85-99.
IEEE DOI
1903
Emotion recognition, Training, Speech, Speech recognition, Acoustics,
Training data, Data models, Emotion recognition, cross-corpus,
multi-task learning
BibRef
Zhang, Y.,
Qian, Y.,
Wu, D.,
Hossain, M.S.,
Ghoneim, A.,
Chen, M.,
Emotion-Aware Multimedia Systems Security,
MultMed(21), No. 3, March 2019, pp. 617-624.
IEEE DOI
1903
authorisation, cloud computing, data privacy, emotion recognition,
learning (artificial intelligence), multimedia systems,
social robot
BibRef
Hu, M.[Min],
Wang, H.[Haowen],
Wang, X.H.[Xiao-Hua],
Yang, J.[Juan],
Wang, R.G.[Rong-Gui],
Video facial emotion recognition based on local enhanced motion
history image and CNN-CTSLSTM networks,
JVCIR(59), 2019, pp. 176-185.
Elsevier DOI
1903
Video emotion recognition, Motion history image, LSTM, Facial landmarks
BibRef
Navarathna, R.,
Carr, P.,
Lucey, P.,
Matthews, I.,
Estimating Audience Engagement to Predict Movie Ratings,
AffCom(10), No. 1, January 2019, pp. 48-59.
IEEE DOI
1903
Motion pictures, Biomedical monitoring, Music, Computer vision, Skin,
Lighting, Face, Audience, behaviour, engagement, movie, film
BibRef
Khan, G.[Gulraiz],
Siddiqi, A.[Aiman],
Khan, M.U.G.[Muhammad Usman Ghani],
Wahla, S.Q.[Samyan Qayyum],
Samyan, S.[Sahar],
Geometric positions and optical flow based emotion detection using MLP
and reduced dimensions,
IET-IPR(13), No. 4, March 2019, pp. 634-643.
DOI Link
1903
BibRef
Jain, D.K.[Deepak Kumar],
Shamsolmoali, P.[Pourya],
Sehdev, P.[Paramjit],
Extended deep neural network for facial emotion recognition,
PRL(120), 2019, pp. 69-74.
Elsevier DOI
1904
Facial emotion recognition, Deep neural network, Fully convolution network
BibRef
Li, S.[Shan],
Deng, W.H.[Wei-Hong],
Blended Emotion in-the-Wild: Multi-label Facial Expression Recognition
Using Crowdsourced Annotations and Deep Locality Feature Learning,
IJCV(127), No. 6-7, June 2019, pp. 884-906.
Springer DOI
1906
BibRef
Earlier:
Deep Emotion Transfer Network for Cross-database Facial Expression
Recognition,
ICPR18(3092-3099)
IEEE DOI
1812
Databases, Face recognition, Kernel, Training, Task analysis, Testing
BibRef
Kollias, D.[Dimitrios],
Tzirakis, P.[Panagiotis],
Nicolaou, M.A.[Mihalis A.],
Papaioannou, A.[Athanasios],
Zhao, G.Y.[Guo-Ying],
Schuller, B.[Björn],
Kotsia, I.[Irene],
Zafeiriou, S.P.[Stefanos P.],
Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge,
Deep Architectures, and Beyond,
IJCV(127), No. 6-7, June 2019, pp. 907-929.
Springer DOI
1906
BibRef
Earlier: A1, A3, A7, A5, A8, Only:
Recognition of Affect in the Wild Using Deep Neural Networks,
FaceWild17(1972-1979)
IEEE DOI
1709
Computer architecture, Databases, Estimation, Kernel,
Recurrent neural networks, Training, Visualization
BibRef
Zafeiriou, S.P.[Stefanos P.],
Kollias, D.[Dimitrios],
Nicolaou, M.A.[Mihalis A.],
Papaioannou, A.[Athanasios],
Zhao, G.Y.[Guo-Ying],
Kotsia, I.[Irene],
Aff-Wild: Valence and Arousal 'In-the-Wild' Challenge,
FaceWild17(1980-1987)
IEEE DOI
1709
Databases, Distance measurement, Estimation, Motion pictures, Tools,
Videos, YouTube
BibRef
Kim, Y.,
Provost, E.M.,
ISLA: Temporal Segmentation and Labeling for Audio-Visual Emotion
Recognition,
AffCom(10), No. 2, April 2019, pp. 196-208.
IEEE DOI
1906
Speech, Emotion recognition, Speech recognition, Correlation,
Labeling, Eyebrows, Visualization, Audio-visual, emotion, recognition,
speech
BibRef
Barrett, L.F.,
Adolphs, R.,
Marsella, S.,
Martinez, A.,
Pollak, S.D.,
Emotional expressions reconsidered:
Challenges to inferring emotion from human facial movements,
OtherPsychological Science in the Public Interest 2018.
WWW Link.
1907
BibRef
Wang, X.H.[Xiao-Hua],
Peng, M.[Muzi],
Pan, L.J.[Li-Juan],
Hu, M.[Min],
Jin, C.H.[Chun-Hua],
Ren, F.[Fuji],
Two-level attention with two-stage multi-task learning for facial
emotion recognition,
JVCIR(62), 2019, pp. 217-225.
Elsevier DOI
1908
BibRef
Earlier:
Two-Level Attention with Multi-task Learning for Facial Emotion
Estimation,
MMMod19(I:227-238).
Springer DOI
1901
Facial emotion recognition, Attention mechanism,
Multi-task learning, Valence-arousal dimension
BibRef
Soroush, M.Z.[Morteza Zangeneh],
Maghooli, K.[Keivan],
Setarehdan, S.K.[Seyed Kamaledin],
Nasrabadi, A.M.[Ali Motie],
A novel EEG-based approach to classify emotions through phase space
dynamics,
SIViP(13), No. 6, September 2019, pp. 1149-1156.
Springer DOI
1908
BibRef
Zhu, X.L.[Xue-Lin],
Cao, B.W.[Bi-Wei],
Xu, S.[Shuai],
Liu, B.[Bo],
Cao, J.X.[Jiu-Xin],
Joint Visual-Textual Sentiment Analysis Based on Cross-Modality
Attention Mechanism,
MMMod19(I:264-276).
Springer DOI
1901
BibRef
Sapinski, T.[Tomasz],
Kaminska, D.[Dorota],
Pelikant, A.[Adam],
Ozcinar, C.[Cagri],
Avots, E.[Egils],
Anbarjafari, G.[Gholamreza],
Multimodal Database of Emotional Speech, Video and Gestures,
MIPPSNA18(153-163).
Springer DOI
1901
Dataset, Emotions.
BibRef
Gonzalez-Diaz, R.[Rocio],
Paluzo-Hidalgo, E.[Eduardo],
Quesada, J.F.[José F.],
Towards Emotion Recognition: A Persistent Entropy Application,
CTIC19(96-109).
Springer DOI
1901
BibRef
Vicol, P.[Paul],
Tapaswi, M.[Makarand],
Castrejón, L.[Lluís],
Fidler, S.[Sanja],
MovieGraphs:
Towards Understanding Human-Centric Situations from Videos,
CVPR18(8581-8590)
IEEE DOI
1812
Videos of social situations to teach robots to understand people.
Videos, Motion pictures, Semantics, Natural languages, Face,
Automobiles, Legged locomotion
BibRef
Nguyen, D.,
Nguyen, K.,
Sridharan, S.,
Abbasnejad, I.,
Dean, D.,
Fookes, C.,
Meta Transfer Learning for Facial Emotion Recognition,
ICPR18(3543-3548)
IEEE DOI
1812
Task analysis, Emotion recognition, Face recognition,
Feature extraction, Face, Computer architecture
BibRef
Kumar, R.K.,
Garain, J.,
Kisku, D.R.,
Sanyal, G.,
Estimating Attention of Faces Due to its Growing Level of Emotions,
Cognitive18(2033-20338)
IEEE DOI
1812
Feature extraction, Training, Databases, Task analysis,
Visualization, Convolution, Testing
BibRef
Xu, J.,
Dong, Y.,
Ma, L.,
Bai, H.,
Video-based Emotion Recognition using Aggregated Features and
Spatio-temporal Information,
ICPR18(2833-2838)
IEEE DOI
1812
Feature extraction, Videos, Emotion recognition,
Task analysis, Face recognition, Face
BibRef
Tautkute, I.,
Trzcinski, T.,
Bielski, A.,
I Know How You Feel: Emotion Recognition with Facial Landmarks,
WiCV18(1959-19592)
IEEE DOI
1812
Emotion recognition, Task analysis, Face, Face recognition,
Convolutional neural networks, Databases
BibRef
Panda, R.[Rameswar],
Zhang, J.M.[Jian-Ming],
Li, H.X.[Hao-Xiang],
Lee, J.Y.[Joon-Young],
Lu, X.[Xin],
Roy-Chowdhury, A.K.[Amit K.],
Contemplating Visual Emotions:
Understanding and Overcoming Dataset Bias,
ECCV18(II: 594-612).
Springer DOI
1810
BibRef
Balouchian, P.,
Foroosh, H.[Hassan],
Context-Sensitive Single-Modality Image Emotion Analysis: A Unified
Architecture from Dataset Construction to CNN Classification,
ICIP18(1932-1936)
IEEE DOI
1809
Emotion recognition, Training, Visualization,
Context modeling, Single-Modality Classification
BibRef
Yagis, E.[Ekin],
Unel, M.[Mustafa],
Facial Expression Based Emotion Recognition Using Neural Networks,
ICIAR18(210-217).
Springer DOI
1807
BibRef
Abuhammad, H.[Huthaifa],
Everson, R.[Richard],
Emotional Faces in the Wild: Feature Descriptors for Emotion
Classification,
ICIAR18(164-174).
Springer DOI
1807
BibRef
Torres-Valencia, C.,
Alvarez-Meza, A.,
Orozco-Gutierrez, A.,
Emotion Assessment Using Adaptive Learning-Based Relevance Analysis,
ICIAR18(193-200).
Springer DOI
1807
BibRef
Gaus, Y.F.A.,
Meng, H.,
Linear and Non-Linear Multimodal Fusion for Continuous Affect
Estimation In-the-Wild,
FG18(492-498)
IEEE DOI
1806
Correlation, Emotion recognition, Estimation, Feature extraction,
Linear regression, Mathematical model, Predictive models, affect,
non-linear
BibRef
Sokolov, D.,
Patkin, M.,
Real-Time Emotion Recognition on Mobile Devices,
FG18(787-787)
IEEE DOI
1806
Emotion recognition, Face recognition, Feature extraction,
Machine learning, Mobile handsets, Neural networks,
mobile devices
BibRef
Shamsi, S.N.,
Singh, B.P.,
Wadhwa, M.,
Group Affect Prediction Using Multimodal Distributions,
Assist18(77-83)
IEEE DOI
1806
convolution, emotion recognition, feature extraction,
feedforward neural nets, object detection, CNN model,
Training
BibRef
Kowalski, M.,
Nasarzewski, Z.,
Galinski, G.,
Garbat, P.,
HoloFace: Augmenting Human-to-Human Interactions on HoloLens,
WACV18(141-149)
IEEE DOI
1806
emotion recognition, face recognition, pose estimation,
solid modelling, HoloFace framework, HoloLens user,
BibRef
Knyazev, B.,
Shvetsov, R.,
Efremova, N.,
Kuharenko, A.,
Leveraging Large Face Recognition Data for Emotion Classification,
FG18(692-696)
IEEE DOI
1806
Emotion recognition, Face, Face recognition,
Support vector machines, Task analysis, Training, Videos,
video processing
BibRef
Lai, Y.H.,
Lai, S.H.,
Emotion-Preserving Representation Learning via Generative Adversarial
Network for Multi-View Facial Expression Recognition,
FG18(263-270)
IEEE DOI
1806
Emotion recognition, Face, Face recognition, Feature extraction,
Generators, face frontalization,
pose variation
BibRef
Loob, C.[Christer],
Rasti, P.[Pejman],
Lüsi, I.[Iiris],
Jacques Junior, J.C.S.[Julio C.S.],
Baró, X.[Xavier],
Escalera, S.[Sergio],
Sapinski, T.[Tomasz],
Kaminska, D.[Dorota],
Anbarjafari, G.[Gholamreza],
Dominant and Complementary Multi-Emotional Facial Expression
Recognition Using C-Support Vector Classification,
FG17(833-838)
IEEE DOI
1707
Databases, Emotion recognition, Face, Face recognition,
Feature extraction, Kernel, Support vector machines
BibRef
Kim, Y.G.,
Huynh, X.P.,
Discrimination Between Genuine Versus Fake Emotion Using Long-Short
Term Memory with Parametric Bias and Facial Landmarks,
EmotionComp17(3065-3072)
IEEE DOI
1802
Boosting, Emotion recognition, Face, Face recognition, Mirrors,
Neurons, Training
BibRef
Saxen, F.,
Werner, P.,
Al-Hamadi, A.,
Real vs. Fake Emotion Challenge: Learning to Rank Authenticity from
Facial Activity Descriptors,
EmotionComp17(3073-3078)
IEEE DOI
1802
Computational modeling, Computer vision, Estimation, Gold,
Support vector machines, Training, Videos
BibRef
Ozkan, S.,
Akar, G.B.,
Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake
Expression Prediction,
EmotionComp17(3094-3100)
IEEE DOI
1802
Computational modeling, Computer architecture, Data models, Face,
Feature extraction, Robustness, Visualization
BibRef
Li, L.,
Baltrusaitis, T.,
Sun, B.,
Morency, L.P.,
Combining Sequential Geometry and Texture Features for Distinguishing
Genuine and Deceptive Emotions,
EmotionComp17(3147-3153)
IEEE DOI
1802
Emotion recognition, Face, Image reconstruction, Logic gates,
Mathematical model, Visualization
BibRef
Wan, J.,
Escalera, S.,
Anbarjafari, G.,
Escalante, H.J.,
Baro, X.,
Guyon, I.,
Madadi, M.,
Allik, J.,
Gorbova, J.,
Lin, C.,
Xie, Y.,
Results and Analysis of ChaLearn LAP Multi-modal Isolated and
Continuous Gesture Recognition, and Real Versus Fake Expressed
Emotions Challenges,
EmotionComp17(3189-3197)
IEEE DOI
1802
Emotion recognition, Feature extraction, Gesture recognition,
Skeleton, Spatiotemporal phenomena, Training
BibRef
Moghadam, S.M.,
Seyyedsalehi, S.A.,
Nonlinear analysis of video images using deep recurrent
auto-associative neural networks for facial understanding,
IPRIA17(20-25)
IEEE DOI
1712
emotion recognition, face recognition, feature extraction,
image sequences, learning (artificial intelligence),
Nonlinear Video Analysis
BibRef
Li, J.S.[Jian-Shu],
Chen, Y.P.[Yun-Peng],
Xiao, S.T.[Sheng-Tao],
Zhao, J.[Jian],
Roy, S.[Sujoy],
Feng, J.S.[Jia-Shi],
Yan, S.C.[Shui-Cheng],
Sim, T.[Terence],
Estimation of Affective Level in the Wild with Multiple Memory
Networks,
FaceWild17(1947-1954)
IEEE DOI
1709
Bidirectional control, Estimation, Face, Feature extraction,
Neural networks, Predictive models, Training
See also Task Relation Networks.
BibRef
Kosti, R.,
Alvarez, J.M.,
Recasens, A.,
Lapedriza, A.[Agata],
Emotion Recognition in Context,
CVPR17(1960-1968)
IEEE DOI
1711
BibRef
Earlier:
EMOTIC: Emotions in Context Dataset,
DeepAffective17(2309-2317)
IEEE DOI
1709
Computer vision, Databases, Distance measurement,
Emotion recognition, Face, Face recognition, Image, recognition
BibRef
Ruiz, A.[Adria],
Martinez, O.[Oriol],
Binefa, X.[Xavier],
Sukno, F.M.[Federico M.],
Fusion of Valence and Arousal Annotations through Dynamic Subjective
Ordinal Modelling,
FG17(331-338)
IEEE DOI
1707
Reliable ground truth.
Affective computing, Computer vision, Context, Labeling, Observers,
Training, Videos
BibRef
McDuff, D.,
Soleymani, M.,
Large-scale Affective Content Analysis:
Combining Media Content Features and Facial Reactions,
FG17(339-345)
IEEE DOI
1707
Encoding, Feature extraction, Media, Software, Tagging, Videos, Visualization
BibRef
Guo, J.,
Zhou, S.,
Wu, J.,
Wan, J.,
Zhu, X.,
Lei, Z.,
Li, S.Z.,
Multi-modality Network with Visual and Geometrical Information for
Micro Emotion Recognition,
FG17(814-819)
IEEE DOI
1707
Computer architecture, Emotion recognition, Face, Face recognition,
Feature extraction, Geometry, Visualization
BibRef
Talavera, E.[Estefania],
Strisciuglio, N.[Nicola],
Petkov, N.[Nicolai],
Radeva, P.[Petia],
Sentiment Recognition in Egocentric Photostreams,
IbPRIA17(471-479).
Springer DOI
1706
BibRef
Zhao, K.[Kun],
Yang, S.[Siqi],
Wiliem, A.,
Lovell, B.C.,
Landmark manifold: Revisiting the Riemannian manifold approach for
facial emotion recognition,
ICPR16(1095-1100)
IEEE DOI
1705
Emotion recognition, Feature extraction, Kernel, Manifolds,
Measurement, Shape, Support, vector, machines
BibRef
Noroozi, F.,
Marjanovic, M.,
Njegus, A.,
Escalera, S.,
Anbarjafari, G.,
Fusion of classifier predictions for audio-visual emotion recognition,
ICPR16(61-66)
IEEE DOI
1705
Databases, Emotion recognition, Eyebrows, Face, Feature extraction,
Mouth, Visualization
BibRef
Laranjeira, A.[Ana],
Frazăo, X.[Xavier],
Pimentel, A.[André],
Ribeiro, B.[Bernardete],
How Deep Can We Rely on Emotion Recognition,
CIARP16(511-520).
Springer DOI
1703
BibRef
Niu, J.W.[Jian-Wei],
Su, Y.M.[Yi-Ming],
Mo, S.[Shasha],
Zhu, Z.Y.[Ze-Yu],
A Novel Affective Visualization System for Videos Based on Acoustic and
Visual Features,
MMMod17(II: 15-27).
Springer DOI
1701
BibRef
Wang, G.L.[Guo-Long],
Qin, Z.[Zheng],
Xu, K.P.[Kai-Ping],
Recognizing Emotions Based on Human Actions in Videos,
MMMod17(II: 306-317).
Springer DOI
1701
BibRef
Kim, B.K.,
Dong, S.Y.,
Roh, J.,
Kim, G.,
Lee, S.Y.,
Fusing Aligned and Non-aligned Face Information for Automatic Affect
Recognition in the Wild: A Deep Learning Approach,
Affect16(1499-1508)
IEEE DOI
1612
BibRef
Mou, W.,
Gunes, H.[Hatice],
Patras, I.,
Automatic Recognition of Emotions and Membership in Group Videos,
Affect16(1478-1486)
IEEE DOI
1612
BibRef
Benitez-Quiroz, C.F.,
Srinivasan, R.,
Martinez, A.M.,
EmotioNet: An Accurate, Real-Time Algorithm for the Automatic
Annotation of a Million Facial Expressions in the Wild,
CVPR16(5562-5570)
IEEE DOI
1612
BibRef
Allaert, B.[Benjamin],
Mennesson, J.[José],
Bilasco, I.M.[Ioan Marius],
EmoGame: Towards a Self-Rewarding Methodology for Capturing Children
Faces in an Engaging Context,
HBU16(3-14).
Springer DOI
1611
BibRef
Mennesson, J.[José],
Allaert, B.[Benjamin],
Bilasco, I.M.[Ioan Marius],
van der Aa, N.,
Denis, A.,
Cruz-Lara, S.,
Faces and thoughts: An empathic dairy,
FG15(1-1)
IEEE DOI
1508
cameras
BibRef
Khorrami, P.,
Le Paine, T.,
Brady, K.,
Dagli, C.,
Huang, T.S.,
How deep neural networks can improve emotion recognition on video
data,
ICIP16(619-623)
IEEE DOI
1610
Convolution
BibRef
Peng, K.C.,
Sadovnik, A.,
Gallagher, A.,
Chen, T.,
Where do emotions come from? Predicting the Emotion Stimuli Map,
ICIP16(614-618)
IEEE DOI
1610
Benchmark testing
BibRef
Zhang, H.,
Xu, M.,
Modeling temporal information using discrete fourier transform for
recognizing emotions in user-generated videos,
ICIP16(629-633)
IEEE DOI
1610
Discrete Fourier transforms
BibRef
Ranganathan, H.,
Chakraborty, S.,
Panchanathan, S.,
Multimodal emotion recognition using deep learning architectures,
WACV16(1-9)
IEEE DOI
1606
Databases
BibRef
Deng, W.,
Hu, J.,
Zhang, S.,
Guo, J.,
DeepEmo: Real-world facial expression analysis via deep learning,
VCIP15(1-4)
IEEE DOI
1605
Databases
BibRef
Khan, R.A.[Rizwan Ahmed],
Meyer, A.[Alexandre],
Bouakaz, S.[Saida],
Automatic Affect Analysis: From Children to Adults,
ISVC15(II: 304-313).
Springer DOI
1601
BibRef
Ding, X.M.[Xin-Miao],
Huang, L.[Lulu],
Li, B.[Bing],
Lang, C.Y.[Cong-Yan],
Hua, Z.[Zhen],
Wang, Y.L.[Yu-Ling],
A Novel Emotional Saliency Map to Model Emotional Attention Mechanism,
MMMod16(II: 197-206).
Springer DOI
1601
BibRef
Chen, M.[Ming],
Zhang, L.[Lu],
Allebach, J.P.[Jan P.],
Learning deep features for image emotion classification,
ICIP15(4491-4495)
IEEE DOI
1512
Image emotion
BibRef
Zhao, S.C.[Si-Cheng],
Yao, H.X.[Hong-Xun],
Jiang, X.L.[Xiao-Lei],
Sun, X.S.[Xiao-Shuai],
Predicting discrete probability distribution of image emotions,
ICIP15(2459-2463)
IEEE DOI
1512
Emotion Distribution Prediction; Image Emotion; Sparse Learning
BibRef
Happy, S.L.,
Routray, A.,
Robust facial expression classification using shape and appearance
features,
ICAPR15(1-5)
IEEE DOI
1511
emotion recognition
BibRef
Chen, H.C.[Hsieh-Chung],
Comiter, M.Z.[Marcus Z.],
Kung, H.T.,
McDanel, B.[Bradley],
Sparse Coding Trees with application to emotion classification,
AMFG15(77-86)
IEEE DOI
1510
Accuracy
BibRef
Chen, H.[Hui],
Li, J.D.[Jiang-Dong],
Zhang, F.J.[Feng-Jun],
Li, Y.[Yang],
Wang, H.A.[Hong-An],
3D model-based continuous emotion recognition,
CVPR15(1836-1845)
IEEE DOI
1510
BibRef
Alashkar, T.[Taleb],
Ben Amor, B.[Boulbaba],
Berretti, S.[Stefano],
Daoudi, M.[Mohamed],
Analyzing trajectories on Grassmann manifold for early emotion
detection from depth videos,
FG15(1-6)
IEEE DOI
1508
emotion recognition
See also Grassmann framework for 4D facial shape analysis, A.
BibRef
Save, M.,
Zazula, D.,
Munda, J.,
Potocnik, B.,
Semi-transparent mirror with hidden camera to assess human emotions,
FG15(1-1)
IEEE DOI
1508
emotion recognition
BibRef
Chen, J.X.[Ji-Xu],
Chang, M.C.[Ming-Ching],
Tu, P.,
A live video analytic system for affect analysis in public space,
FG15(1-1)
IEEE DOI
1508
emotion recognition
BibRef
Vieriu, R.L.[Radu-Laurentiu],
Tulyakov, S.[Sergey],
Semeniuta, S.,
Sangineto, E.,
Sebe, N.,
Facial expression recognition under a wide range of head poses,
FG15(1-7)
IEEE DOI
1508
emotion recognition
BibRef
Kniaz, V.V.,
Smirnova, Z.N.,
Music-Elicited Emotion Identification Using Optical Flow Analysis of
Human Face,
PTVSBB15(27-32).
DOI Link
1508
BibRef
Oussalah, M.,
Wang, S.,
Fuzzy emotion recognition model for video sequences,
IPTA12(127-132)
IEEE DOI
1503
emotion recognition
BibRef
Sonmez, E.B.,
Sankur, B.,
Albayrak, S.,
Classification with emotional faces via a robust sparse classifier,
IPTA12(344-349)
IEEE DOI
1503
emotion recognition
BibRef
Peng, K.C.[Kuan-Chuan],
Karlsson, K.[Kolbeinn],
Chen, T.H.[Tsu-Han],
Zhang, D.Q.[Dong-Qing],
Yu, H.[Heather],
A framework of changing image emotion using emotion prediction,
ICIP14(4637-4641)
IEEE DOI
1502
Correlation
BibRef
Cardone, D.[Daniela],
Merla, A.[Arcangelo],
The thermal dimension of psychophysiological and emotional responses
revealed by thermal infrared imaging,
ICIP14(1942-1946)
IEEE DOI
1502
Biomedical monitoring
BibRef
García, H.F.[Hernán F.],
Álvarez, M.A.[Mauricio A.],
Orozco, Á.Á.[Álvaro Á.],
Gaussian Process Dynamical Models for Emotion Recognition,
ISVC14(II: 799-808).
Springer DOI
1501
BibRef
Rezazadegan Tavakoli, H.[Hamed],
Yanulevskaya, V.[Victoria],
Rahtu, E.[Esa],
Heikkila, J.[Janne],
Sebe, N.[Nicu],
Emotional Valence Recognition, Analysis of Salience and Eye Movements,
ICPR14(4666-4671)
IEEE DOI
1412
Computational modeling
BibRef
Lablack, A.[Adel],
Danisman, T.[Taner],
Bilasco, l.M.[loan Marius],
Djeraba, C.[Chaabane],
A Local Approach for Negative Emotion Detection,
ICPR14(417-420)
IEEE DOI
1412
Computer vision
BibRef
Araujo, R.[Rodrigo],
Kamel, M.S.[Mohamed S.],
Audio-Visual Emotion Analysis Using Semi-Supervised Temporal Clustering
with Constraint Propagation,
ICIAR14(II: 3-11).
Springer DOI
1410
BibRef
Ramirez, G.A.[Geovany A.],
Fuentes, O.[Olac],
Crites, S.L.[Stephen L.],
Jimenez, M.[Maria],
Ordonez, J.[Juanita],
Color Analysis of Facial Skin: Detection of Emotional State,
SocialInter14(474-479)
IEEE DOI
1409
Skin color.
BibRef
Acar, E.[Esra],
Hopfgartner, F.[Frank],
Albayrak, S.[Sahin],
Understanding Affective Content of Music Videos through Learned
Representations,
MMMod14(I: 303-314).
Springer DOI
1405
BibRef
Hadjerci, O.[Oussama],
Lablack, A.[Adel],
Bilasco, I.M.[Ioan Marius],
Djeraba, C.[Chaabane],
Affect Recognition Using Magnitude Models of Motion,
MMMod14(II: 339-344).
Springer DOI
1405
BibRef
Huang, X.H.[Xiao-Hua],
Zhao, G.Y.[Guo-Ying],
Pietikainen, M.[Matti],
Emotion recognition from facial images with arbitrary views,
BMVC13(xx-yy).
DOI Link
1402
BibRef
Nguyen, H.[Hung],
Kotani, K.[Kazunori],
Chen, F.[Fan],
Le, B.[Bac],
A Thermal Facial Emotion Database and Its Analysis,
PSIVT13(397-408).
Springer DOI
1402
Dataset, Facial Expression.
BibRef
Behrens, S.[Stephanie],
Al-Hamadi, A.[Ayoub],
Niese, R.[Robert],
Redweik, E.[Eicke],
Automatic User-Specific Avatar Parametrisation and Emotion Mapping,
ACIVS13(192-202).
Springer DOI
1311
Classification and Recognition
BibRef
Poggi, I.[Isabella],
d'Errico, F.[Francesca],
Towards the Parody Machine. Qualitative Analysis and Cognitive
Processes in the Parody of a Politician,
SBA13(491-500).
Springer DOI
1309
BibRef
Jeni, L.A.[László Attila],
Lorincz, A.[András],
Szabó, Z.[Zoltán],
Cohn, J.F.[Jeffrey F.],
Kanade, T.[Takeo],
Spatio-temporal Event Classification Using Time-Series Kernel Based
Structured Sparsity,
ECCV14(IV: 135-150).
Springer DOI
1408
BibRef
Earlier: A2, A1, A3, A4, A5:
Emotional Expression Classification Using Time-Series Kernels,
AMFG13(889-895)
IEEE DOI
1309
3d shape
BibRef
Chen, S.Z.[Shi-Zhi],
Tian, Y.L.[Ying-Li],
Margin-constrained multiple kernel learning based multi-modal fusion
for affect recognition,
FG13(1-7)
IEEE DOI
1309
face recognition
BibRef
Yu, X.[Xiang],
Zhang, S.T.[Shao-Ting],
Yu, Y.[Yang],
Dunbar, N.,
Jensen, M.,
Burgoon, J.K.,
Metaxas, D.N.,
Automated analysis of interactional synchrony using robust facial
tracking and expression recognition,
FG13(1-6)
IEEE DOI
1309
emotion recognition
BibRef
Cheng, J.K.[Jin-Kuang],
Deng, Y.D.[Yang-Dong],
Meng, H.Y.[Hong-Ying],
Wang, Z.H.[Zhi-Hua],
A facial expression based continuous emotional state monitoring
system with GPU acceleration,
FG13(1-6)
IEEE DOI
1309
emotion recognition
BibRef
Levesque, J.C.,
Morency, L.P.[Louis-Philippe],
Gagne, C.,
Sequential emotion recognition using Latent-Dynamic Conditional
Neural Fields,
FG13(1-6)
IEEE DOI
1309
emotion recognition
BibRef
Baltrusaitis, T.,
Banda, N.,
Robinson, P.,
Dimensional affect recognition using Continuous Conditional Random
Fields,
FG13(1-8)
IEEE DOI
1309
emotion recognition
BibRef
Agarwal, S.,
Mukherjee, D.P.,
Decoding mixed emotions from expression map of face images,
FG13(1-6)
IEEE DOI
1309
emotion recognition
BibRef
Marco-Giménez, L.[Luis],
Arevalillo-Herráez, M.[Miguel],
Cuhna-Pérez, C.[Cristina],
Eigenexpressions: Emotion Recognition Using Multiple Eigenspaces,
IbPRIA13(758-765).
Springer DOI
1307
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ICIP14(1376-1380)
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ICIP13(4215-4219)
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ICIP13(4215-4219)
IEEE DOI
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Earlier:
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Avatars.
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See also Multi-Output Laplacian Dynamic Ordinal Regression for Facial Expression Recognition and Intensity Estimation.
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SSPR06(916-922).
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And:
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AMDO06(291-299).
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Earlier:
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ICIAR05(1133-1140).
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0509
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Earlier:
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ICBA04(81-87).
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See also the MIT Mood Meter:
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0408
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ICIP03(I: 721-724).
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See also Facial Expression Recognition from Video Sequences: Temporal and Static Modeling.
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Multimodal Human Emotion/Expression Recognition,
AFGR98(366-371).
IEEE DOI
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9800
Chapter on Face Recognition, Detection, Tracking, Gesture Recognition, Fingerprints, Biometrics continues in
Depression Analysis, PTSD, Mental Health .