Machine Learning Systems for Multimodal Affect Recognition
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[1] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[2] Markus Kächele,et al. The Influence of Annotation, Corpus Design, and Evaluation on the Outcome of Automatic Classification of Human Emotions , 2016, Front. ICT.
[3] Albert Ali Salah,et al. Ensemble CCA for Continuous Emotion Prediction , 2014, AVEC '14.
[4] Markus Kächele,et al. Paradigms for the Construction and Annotation of Emotional Corpora for Real-world Human-Computer-Interaction , 2015, ICPRAM.
[5] Detection of Emotional Events utilizing Support Vector Methods in an Active Learning HCI Scenario , 2014, ERM4HCI '14.
[6] Ville Ojansivu,et al. Blur Insensitive Texture Classification Using Local Phase Quantization , 2008, ICISP.
[7] P. Gomez,et al. Affective and physiological responses to environmental noises and music. , 2004, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[8] Patrick Thiam,et al. Adaptive confidence learning for the personalization of pain intensity estimation systems , 2017, Evol. Syst..
[9] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[10] Markus Kächele,et al. Support Vector Regression of Sparse Dictionary-Based Features for View-Independent Action Unit Intensity Estimation , 2017, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).
[11] Tanu Sharma,et al. A novel feature extraction for robust EMG pattern recognition , 2016, Journal of medical engineering & technology.
[12] S. Sathiya Keerthi,et al. Convergence of a Generalized SMO Algorithm for SVM Classifier Design , 2002, Machine Learning.
[13] R. Treister,et al. Differentiating between heat pain intensities: The combined effect of multiple autonomic parameters , 2012, PAIN®.
[14] Nitesh V. Chawla,et al. SMOTEBoost: Improving Prediction of the Minority Class in Boosting , 2003, PKDD.
[15] Sascha Meudt,et al. Machine Learning Driven Heart Rate Detection with Camera Photoplethysmography in Time Domain , 2016, ANNPR.
[16] Alexander J. Smola,et al. Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.
[17] Tara N. Sainath,et al. Learning filter banks within a deep neural network framework , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.
[18] Gustavo Moreira da Silva,et al. Automatic pain quantification using autonomic parameters , 2014 .
[19] Thierry Dutoit,et al. Causal-anticausal decomposition of speech using complex cepstrum for glottal source estimation , 2011, Speech Commun..
[20] Jennifer Healey,et al. Affective wearables , 1997, Digest of Papers. First International Symposium on Wearable Computers.
[21] C. Vinola,et al. A Survey on Human Emotion Recognition Approaches, Databases and Applications , 2015 .
[22] Razvan Pascanu,et al. Combining modality specific deep neural networks for emotion recognition in video , 2013, ICMI '13.
[23] Honglak Lee,et al. Unsupervised feature learning for audio classification using convolutional deep belief networks , 2009, NIPS.
[24] M. Chraif,et al. Correlative study between the personality factors and pain perception at young students at psychology , 2015 .
[25] P. Alku,et al. Normalized amplitude quotient for parametrization of the glottal flow. , 2002, The Journal of the Acoustical Society of America.
[26] Navdeep Jaitly,et al. Hybrid speech recognition with Deep Bidirectional LSTM , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.
[27] Daniel Gatica-Perez,et al. Latent semantic analysis of facial action codes for automatic facial expression recognition , 2004, MIR '04.
[28] Günther Palm,et al. Multiple classifier combination using reject options and markov fusion networks , 2012, ICMI '12.
[29] Markus Kächele,et al. Multiple Classifier Systems for the Classification of Audio-Visual Emotional States , 2011, ACII.
[30] Patrick Thiam,et al. Continuous Multimodal Human Affect Estimation using Echo State Networks , 2016, AVEC@ACM Multimedia.
[31] Louis-Philippe Morency,et al. Automatic Nonverbal Behavior Indicators of Depression and PTSD: Exploring Gender Differences , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.
[32] Dongmei Jiang,et al. Kalman Filter-Based Facial Emotional Expression Recognition , 2011, ACII.
[33] Roddy Cowie,et al. FEELTRACE: an instrument for recording perceived emotion in real time , 2000 .
[34] Stefan Wermter,et al. Face expression recognition with a 2-channel Convolutional Neural Network , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[35] Paavo Alku,et al. Comparison of multiple voice source parameters in different phonation types , 2007, INTERSPEECH.
[36] T. Başar,et al. A New Approach to Linear Filtering and Prediction Problems , 2001 .
[37] Stephen Kwek,et al. Applying Support Vector Machines to Imbalanced Datasets , 2004, ECML.
[38] Markus Kächele,et al. Speeding up k-means by approximating Euclidean distances via block vectors , 2016, ICML.
[39] Jacob Cohen. A Coefficient of Agreement for Nominal Scales , 1960 .
[40] Günther Palm,et al. On the discovery of events in EEG data utilizing information fusion , 2013, Comput. Stat..
[41] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[42] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[43] Stefan J. Kiebel,et al. Re-visiting the echo state property , 2012, Neural Networks.
[44] P. Ekman,et al. Pan-Cultural Elements in Facial Displays of Emotion , 1969, Science.
[45] M. Lugger,et al. Classification of different speaking groups ITG Fachtagung Sprachkommunikation 2006 CLASSIFICATION OF DIFFERENT SPEAKING GROUPS BY MEANS OF VOICE QUALITY PARAMETERS , 2011 .
[46] Josef Kittler,et al. Floating search methods in feature selection , 1994, Pattern Recognit. Lett..
[47] Zhihong Zeng,et al. A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[48] Matti Pietikäinen,et al. Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[49] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[50] Hynek Hermansky,et al. RASTA-PLP speech analysis technique , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[51] P. Costa,et al. Revised NEO Personality Inventory (NEO-PI-R) and NEO-Five-Factor Inventory (NEO-FFI) , 1992 .
[52] Patrick Thiam,et al. Fusion Mappings for Multimodal Affect Recognition , 2015, 2015 IEEE Symposium Series on Computational Intelligence.
[53] Hatice Gunes,et al. Automatic Segmentation of Spontaneous Data using Dimensional Labels from Multiple Coders , 2010 .
[54] Markus Kächele,et al. Classification of Emotional States in a Woz Scenario Exploiting Labeled and Unlabeled Bio-physiological Data , 2011, PSL.
[55] Markus Kächele,et al. Semi-Supervised Dictionary Learning of Sparse Representations for Emotion Recognition , 2013, PSL.
[56] Tamás D. Gedeon,et al. Emotion Recognition In The Wild Challenge 2014: Baseline, Data and Protocol , 2014, ICMI.
[57] Hui Han,et al. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.
[58] B. Parkinson,et al. Emotion and motivation , 1995 .
[59] Javier Hernandez,et al. Call Center Stress Recognition with Person-Specific Models , 2011, ACII.
[60] Simon Lucey,et al. Deformable Model Fitting by Regularized Landmark Mean-Shift , 2010, International Journal of Computer Vision.
[61] Markus Kächele,et al. SMO Lattices for the Parallel Training of Support Vector Machines , 2015, ESANN.
[62] Thomas F. Quatieri,et al. Vocal and Facial Biomarkers of Depression based on Motor Incoordination and Timing , 2014, AVEC '14.
[63] Panagiotis K. Artemiadis,et al. An EMG-Based Robot Control Scheme Robust to Time-Varying EMG Signal Features , 2010, IEEE Transactions on Information Technology in Biomedicine.
[64] James C. Bezdek,et al. Decision templates for multiple classifier fusion: an experimental comparison , 2001, Pattern Recognit..
[65] Ayoub Al-Hamadi,et al. The biovid heat pain database data for the advancement and systematic validation of an automated pain recognition system , 2013, 2013 IEEE International Conference on Cybernetics (CYBCO).
[66] Patrick Thiam,et al. Ensembles of Support Vector Data Description for Active Learning Based Annotation of Affective Corpora , 2015, 2015 IEEE Symposium Series on Computational Intelligence.
[67] Fan Zhang,et al. Automatic affective dimension recognition from naturalistic facial expressions based on wavelet filtering and PLS regression , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[68] Patrick Thiam,et al. Majority-Class Aware Support Vector Domain Oversampling for Imbalanced Classification Problems , 2014, ANNPR.
[69] Adam Kowalczyk,et al. Extreme re-balancing for SVMs: a case study , 2004, SKDD.
[70] Maja Pantic,et al. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON AFFECTIVE COMPUTING , 2022 .
[71] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[72] Qiang Ji,et al. Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[73] Bin Yang,et al. The Relevance of Voice Quality Features in Speaker Independent Emotion Recognition , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.
[74] Robert P. W. Duin,et al. Support vector domain description , 1999, Pattern Recognit. Lett..
[75] Herbert F. Jelinek,et al. Principal component analysis of heart rate variability data in assessing cardiac autonomic neuropathy , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[76] Tanaya Guha,et al. Multimodal Prediction of Affective Dimensions and Depression in Human-Computer Interactions , 2014, AVEC '14.
[77] Fabien Ringeval,et al. Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[78] O. Vassend,et al. Five-factor personality traits and pain sensitivity: A twin study , 2013, PAIN®.
[79] Markus Kächele,et al. Monte Carlo Based Importance Estimation of Localized Feature Descriptors for the Recognition of Facial Expressions , 2014, MPRSS.
[80] Markus Kächele,et al. Cascaded Fusion of Dynamic, Spatial, and Textural Feature Sets for Person-Independent Facial Emotion Recognition , 2014, 2014 22nd International Conference on Pattern Recognition.
[81] M. Bradley,et al. Measuring emotion: the Self-Assessment Manikin and the Semantic Differential. , 1994, Journal of behavior therapy and experimental psychiatry.
[82] K. Fingerman,et al. Age and gender differences in adults' descriptions of emotional reactions to interpersonal problems. , 2003, The journals of gerontology. Series B, Psychological sciences and social sciences.
[83] Russel Pears,et al. Synthetic Minority Over-sampling TEchnique (SMOTE) for Predicting Software Build Outcomes , 2014, SEKE.
[84] Jeffrey M Girard,et al. CARMA: Software for continuous affect rating and media annotation. , 2014, Journal of open research software.
[85] M. Bradley,et al. The International Affective Picture System (IAPS) in the study of emotion and attention. , 2007 .
[86] J. Liljencrants,et al. Dept. for Speech, Music and Hearing Quarterly Progress and Status Report a Four-parameter Model of Glottal Flow , 2022 .
[87] Gwen Littlewort,et al. A Prototype for Automatic Recognition of Spontaneous Facial Actions , 2002, NIPS.
[88] Dennis C. Tkach,et al. Study of stability of time-domain features for electromyographic pattern recognition , 2010, Journal of NeuroEngineering and Rehabilitation.
[89] Shashidhar G. Koolagudi,et al. Spectral Features for Emotion Classification , 2009, 2009 IEEE International Advance Computing Conference.
[90] L. H. Anauer,et al. Speech Analysis and Synthesis by Linear Prediction of the Speech Wave , 2000 .
[91] A. Beck,et al. Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. , 1996, Journal of personality assessment.
[92] Lori Lamel,et al. Challenges in real-life emotion annotation and machine learning based detection , 2005, Neural Networks.
[93] John Kane,et al. Wavelet Maxima Dispersion for Breathy to Tense Voice Discrimination , 2013, IEEE Transactions on Audio, Speech, and Language Processing.
[94] Markus Kächele,et al. Using unlabeled data to improve classification of emotional states in human computer interaction , 2013, Journal on Multimodal User Interfaces.
[95] Davide Fossati,et al. Affect detection from non-stationary physiological data using ensemble classifiers , 2014, Evolving Systems.
[96] Mohamed Chetouani,et al. Robust continuous prediction of human emotions using multiscale dynamic cues , 2012, ICMI '12.
[97] Björn W. Schuller,et al. AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge , 2014, AVEC '14.
[98] Maja Pantic,et al. Biologically vs. Logic Inspired Encoding of Facial Actions and Emotions in Video , 2006, 2006 IEEE International Conference on Multimedia and Expo.
[99] Sascha Meudt,et al. Fusion of Audio-visual Features using Hierarchical Classifier Systems for the Recognition of Affective States and the State of Depression , 2014, ICPRAM.
[100] David DeVault,et al. The Distress Analysis Interview Corpus of human and computer interviews , 2014, LREC.
[101] R. Kotov,et al. Personality and depression: explanatory models and review of the evidence. , 2011, Annual review of clinical psychology.
[102] M. Benedek,et al. Decomposition of skin conductance data by means of nonnegative deconvolution , 2010, Psychophysiology.
[103] Liqing Zhang,et al. ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines , 2005, 2005 International Conference on Neural Networks and Brain.
[104] Björn W. Schuller,et al. AVEC 2013: the continuous audio/visual emotion and depression recognition challenge , 2013, AVEC@ACM Multimedia.
[105] Sascha Meudt,et al. Revisiting the EmotiW challenge: how wild is it really? , 2015, Journal on Multimodal User Interfaces.
[106] Zheru Chi,et al. Emotion Recognition in the Wild with Feature Fusion and Multiple Kernel Learning , 2014, ICMI.
[107] Sascha Meudt,et al. Prosodic, Spectral and Voice Quality Feature Selection Using a Long-Term Stopping Criterion for Audio-Based Emotion Recognition , 2014, 2014 22nd International Conference on Pattern Recognition.
[108] Markus Kächele,et al. Inferring Depression and Affect from Application Dependent Meta Knowledge , 2014, AVEC '14.
[109] Biing-Hwang Juang,et al. Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.
[110] Angeliki Metallinou,et al. Annotation and processing of continuous emotional attributes: Challenges and opportunities , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[111] Federico Girosi,et al. An improved training algorithm for support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.
[112] Shiguang Shan,et al. AU-aware Deep Networks for facial expression recognition , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).
[113] Igor Durdanovic,et al. Parallel Support Vector Machines: The Cascade SVM , 2004, NIPS.
[114] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[115] Ayoub Al-Hamadi,et al. Automatic Pain Recognition from Video and Biomedical Signals , 2014, 2014 22nd International Conference on Pattern Recognition.
[116] Ya Li,et al. Multi-scale Temporal Modeling for Dimensional Emotion Recognition in Video , 2014, AVEC '14.
[117] S M Pincus,et al. Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.
[118] Weiting Chen,et al. Measuring complexity using FuzzyEn, ApEn, and SampEn. , 2009, Medical engineering & physics.
[119] Qiang Chen,et al. A Health-IoT Platform Based on the Integration of Intelligent Packaging, Unobtrusive Bio-Sensor, and Intelligent Medicine Box , 2014, IEEE Transactions on Industrial Informatics.
[120] Paul A. Viola,et al. Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[121] K. Scherer. What are emotions? And how can they be measured? , 2005 .
[122] Ailbhe Ní Chasaide,et al. The role of voice quality in communicating emotion, mood and attitude , 2003, Speech Commun..
[123] M. Bradley,et al. Looking at pictures: affective, facial, visceral, and behavioral reactions. , 1993, Psychophysiology.
[124] Takeo Kanade,et al. The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.
[125] Jeffrey F. Cohn,et al. Automatic detection of pain intensity , 2012, ICMI '12.
[126] Vwani P. Roychowdhury,et al. Distributed Parallel Support Vector Machines in Strongly Connected Networks , 2008, IEEE Transactions on Neural Networks.
[127] Markus Kächele,et al. Using Radial Basis Function Neural Networks for Continuous and Discrete Pain Estimation from Bio-physiological Signals , 2016, ANNPR.
[128] Dongmei Jiang,et al. Multimodal Affective Dimension Prediction Using Deep Bidirectional Long Short-Term Memory Recurrent Neural Networks , 2015, AVEC@ACM Multimedia.
[129] Hatice Gunes,et al. Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space , 2011, IEEE Transactions on Affective Computing.
[130] Patrick Thiam,et al. Methods for Person-Centered Continuous Pain Intensity Assessment From Bio-Physiological Channels , 2016, IEEE Journal of Selected Topics in Signal Processing.
[131] Robert P. W. Duin,et al. Uniform Object Generation for Optimizing One-class Classifiers , 2002, J. Mach. Learn. Res..
[132] Inma Hernáez,et al. Feature Analysis and Evaluation for Automatic Emotion Identification in Speech , 2010, IEEE Transactions on Multimedia.
[133] Jennifer Healey,et al. Digital processing of affective signals , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).
[134] Markus Kächele,et al. On the effects of continuous annotation tools and the human factor on the annotation outcome , 2015, ISCT.
[135] Takeo Kanade,et al. Comprehensive database for facial expression analysis , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).
[136] Enrique Argones-Rúa,et al. Audiovisual three-level fusion for continuous estimation of Russell's emotion circumplex , 2013, AVEC@ACM Multimedia.
[137] Robert E. Schapire,et al. A Brief Introduction to Boosting , 1999, IJCAI.
[138] Herbert Jaeger,et al. Optimization and applications of echo state networks with leaky- integrator neurons , 2007, Neural Networks.
[139] Fabien Ringeval,et al. AV+EC 2015: The First Affect Recognition Challenge Bridging Across Audio, Video, and Physiological Data , 2015, AVEC@ACM Multimedia.
[140] Markus Kächele,et al. Data fusion for automated pain recognition , 2015, 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth).
[141] John Kane,et al. Identifying Regions of Non-Modal Phonation Using Features of the Wavelet Transform , 2011, INTERSPEECH.
[142] Fabien Ringeval,et al. AVEC 2016: Depression, Mood, and Emotion Recognition Workshop and Challenge , 2016, AVEC@ACM Multimedia.
[143] Björn W. Schuller,et al. AVEC 2012: the continuous audio/visual emotion challenge , 2012, ICMI '12.
[144] Shiguang Shan,et al. Combining Multiple Kernel Methods on Riemannian Manifold for Emotion Recognition in the Wild , 2014, ICMI.
[145] George Trigeorgis,et al. Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[146] Friedhelm Schwenker,et al. Investigating fuzzy-input fuzzy-output support vector machines for robust voice quality classification , 2013, Comput. Speech Lang..
[147] Andrew Zisserman,et al. Representing shape with a spatial pyramid kernel , 2007, CIVR '07.
[148] Maja Pantic,et al. Action unit detection using sparse appearance descriptors in space-time video volumes , 2011, Face and Gesture 2011.
[149] Friedhelm Schwenker,et al. Kalman Filter Based Classifier Fusion for Affective State Recognition , 2013, MCS.
[150] P. Costa,et al. A contemplated revision of the NEO Five-Factor Inventory , 2004 .
[151] J. Russell,et al. Evidence for a three-factor theory of emotions , 1977 .
[152] Jeffrey F. Cohn,et al. Painful data: The UNBC-McMaster shoulder pain expression archive database , 2011, Face and Gesture 2011.
[153] Patrick Thiam,et al. Ensemble Methods for Continuous Affect Recognition: Multi-modality, Temporality, and Challenges , 2015, AVEC@ACM Multimedia.
[154] Sascha Meudt,et al. A New Multi-class Fuzzy Support Vector Machine Algorithm , 2014, ANNPR.
[155] Albino Nogueiras,et al. Speech emotion recognition using hidden Markov models , 2001, INTERSPEECH.
[156] Maja Pantic,et al. Continuous Pain Intensity Estimation from Facial Expressions , 2012, ISVC.
[157] Xinjie Yu,et al. Introduction to evolutionary algorithms , 2010, The 40th International Conference on Computers & Indutrial Engineering.
[158] G. Palm,et al. Learning of Decision Fusion Mappings for Pattern Recognition , 2006 .
[159] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[160] Ya Li,et al. Long Short Term Memory Recurrent Neural Network based Multimodal Dimensional Emotion Recognition , 2015, AVEC@ACM Multimedia.
[161] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[162] Sascha Meudt,et al. Audio-Visual User Identification in HCI Scenarios , 2014, MPRSS.
[163] Tamás D. Gedeon,et al. Emotion recognition in the wild challenge (EmotiW) challenge and workshop summary , 2013, ICMI '13.
[164] Elisabeth André,et al. Emotion recognition based on physiological changes in music listening , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[165] Ryohei Nakatsu,et al. Emotion Recognition in Speech Using Neural Networks , 2000, Neural Computing & Applications.
[166] Björn W. Schuller,et al. OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.
[167] Antonio Torralba,et al. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.
[168] Carlos Busso,et al. Correcting Time-Continuous Emotional Labels by Modeling the Reaction Lag of Evaluators , 2015, IEEE Transactions on Affective Computing.
[169] Tamás D. Gedeon,et al. Collecting Large, Richly Annotated Facial-Expression Databases from Movies , 2012, IEEE MultiMedia.
[170] D. W. Robinson,et al. A re-determination of the equal-loudness relations for pure tones , 1956 .
[171] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[172] Michel F. Valstar,et al. Local Gabor Binary Patterns from Three Orthogonal Planes for Automatic Facial Expression Recognition , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.
[173] A. Atiya,et al. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.
[174] Roddy Cowie,et al. Tracing Emotion: An Overview , 2012, Int. J. Synth. Emot..
[175] Taghi M. Khoshgoftaar,et al. Experimental perspectives on learning from imbalanced data , 2007, ICML '07.
[176] Björn W. Schuller,et al. LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework , 2013, Image Vis. Comput..
[177] L. Lin,et al. A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.
[178] M. Picheny,et al. Comparison of Parametric Representation for Monosyllabic Word Recognition in Continuously Spoken Sentences , 2017 .
[179] Astrid Paeschke,et al. A database of German emotional speech , 2005, INTERSPEECH.
[180] Gwen Littlewort,et al. Faces of pain: automated measurement of spontaneousallfacial expressions of genuine and posed pain , 2007, ICMI '07.
[181] Heng Wang,et al. Depression recognition based on dynamic facial and vocal expression features using partial least square regression , 2013, AVEC@ACM Multimedia.
[182] John Platt,et al. Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .
[183] Vladimir Pavlovic,et al. Dynamic Probabilistic CCA for Analysis of Affective Behavior and Fusion of Continuous Annotations , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[184] Björn Schuller,et al. Opensmile: the munich versatile and fast open-source audio feature extractor , 2010, ACM Multimedia.
[185] Tsuhan Chen,et al. The painful face - Pain expression recognition using active appearance models , 2009, Image Vis. Comput..
[186] Jean-Philippe Thiran,et al. Prediction of asynchronous dimensional emotion ratings from audiovisual and physiological data , 2015, Pattern Recognit. Lett..
[187] Markus Kächele,et al. Emotion Recognition in Speech with Deep Learning Architectures , 2016, ANNPR.
[188] Josephine Sullivan,et al. One millisecond face alignment with an ensemble of regression trees , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[189] Günther Palm,et al. Sparse activity and sparse connectivity in supervised learning , 2016, J. Mach. Learn. Res..
[190] Say Wei Foo,et al. Classification of stress in speech using linear and nonlinear features , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..
[191] Semyon Slobounov,et al. Application of a novel measure of EEG non-stationarity as ‘Shannon- entropy of the peak frequency shifting’ for detecting residual abnormalities in concussed individuals , 2011, Clinical Neurophysiology.
[192] Patrick Thiam,et al. On Annotation and Evaluation of Multi-modal Corpora in Affective Human-Computer Interaction , 2014, MA3HMI@INTERSPEECH.