]. B. Bibliographie1, F. Abboud, M. Davoine, and . Dang, Facial expression recognition and synthesis based on appearance model, Signal Processing : Image Communication, vol.19, issue.21, pp.723-740, 2004.

F. Abdat, Reconnaissance automatique des émotions par données multimodales : expressions faciales et signaux physiologiques, p.167, 2010.

F. Abdat, C. Maaoui, and A. Pruski, Human-Computer Interaction Using Emotion Recognition from Facial Expression, 2011 UKSim 5th European Symposium on Computer Modeling and Simulation, pp.196-201, 2011.
DOI : 10.1109/EMS.2011.20

P. Allain and E. Francois, Epreuve de reconnaissance d'émotions en situation dynamique, p.105, 1956.

O. Alzoubi, R. A. Calvo, and R. H. Stevens, Classification of eeg for emotion recognition : An adaptive approach, Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence, pp.52-61, 2009.

J. Andreassi, Psychophysiology : Human Behavior & Physiological Response. awrence Erlbaum Associates, p.167, 2000.

M. Armatte, Le statut changeant de la corrélation en économétrie Revue économique, pp.617-631, 1910.

M. Arnold, Emotion and personality, p.11, 1960.

J. N. Bailenson, E. D. Pontikakis, I. B. Mauss, J. J. Gross, M. E. Jabon et al., Real-time classification of evoked emotions using facial feature tracking and physiological responses, International Journal of Human-Computer Studies, vol.66, issue.5, pp.303-317, 1928.
DOI : 10.1016/j.ijhcs.2007.10.011

P. Bard, A diencephalic mecanism for the expression of rage with special reference to the sympathetic nervous system, J Psychol, vol.84, pp.490-515, 1928.

C. Bartneck, Integrating the occ model of emotions in embodied characters, Proceedings of the Workshop on Virtual Conversational Characters : Applications, Methods, and Research Challenges, p.17, 2002.

A. Batliner, R. Huber, and J. Spilker, The Recognition of Emotion, Spoken Language Processing, pp.122-130, 2000.
DOI : 10.1007/978-3-662-04230-4_9

J. Berkson, Application of the logistic function to bio-assay, J Am Stat Assoc - Journal of the American Statistical Association, vol.39, issue.227, pp.357-65, 1944.

V. Billaudeau, L. Diot, A. Trenvouez, and A. Didry, Le recrutement : quelles pratiques actuelles ? : Résultats d'enquête auprès des professionnels du recrutement, p.122, 2012.

C. M. Bishop, Pattern recognition and machine learning, p.54, 2006.

M. J. Black and Y. Yacoob, Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion, Proceedings of IEEE International Conference on Computer Vision, pp.374-381, 1995.
DOI : 10.1109/ICCV.1995.466915

J. Bouchet, Ingénierie de l'interaction multimodale en entrée Approche à composants ICARE, p.167, 2006.

M. Bradley and P. J. Lang, The International Affective Picture System (IAPS) in the Study of Emotion and Attention, p.62, 2007.

M. M. Bradley, B. N. Cuthbert, and .. J. Lang, Picture media and emotion: Effects of a sustained affective context, Psychophysiology, vol.24, issue.2, pp.662-670, 1996.
DOI : 10.1016/0022-1031(84)90047-7

M. M. Bradley and .. J. Lang, Affective reactions to acoustic stimuli, Psychophysiology, vol.37, issue.2, pp.204-215, 2000.
DOI : 10.1111/1469-8986.3720204

P. Broca, Anatomie comparée des circonvolutions cérébrales. le grand lobe limbique et la scissure limbique dans la série des mammifères. Revue d'Anthropologie, pp.385-498, 1878.

S. Buisine, B. Hartmann, M. Mancini, and C. Pelachaud, Conception et ??valuation d'un mod??le d'expressivit?? pour les gestes des agents conversationnels, Revue en Intelligence Artificielle RIA, Special Edition Interaction Emotionnelle, pp.621-663, 2006.
DOI : 10.3166/ria.20.621-638

URL : https://hal.archives-ouvertes.fr/hal-00786518/file/LCPI_RIA_2006_BUISINE.pdf

C. Busso, Z. Deng, S. Yildirim, M. Bulut, C. Lee et al., Analysis of emotion recognition using facial expressions, speech and multimodal information, Proceedings of the 6th international conference on Multimodal interfaces , ICMI '04, pp.205-211, 2004.
DOI : 10.1145/1027933.1027968

J. Cacioppo and L. Tassimary, Inferring psychological significance from physiological signals., American Psychologist, vol.45, issue.1, pp.16-28, 1990.
DOI : 10.1037/0003-066X.45.1.16

R. A. Calvo, I. Brown, and S. Scheding, Effect of Experimental Factors on the Recognition of Affective Mental States through Physiological Measures, Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence, pp.62-70, 2009.
DOI : 10.1007/978-3-642-10439-8_7

R. A. Calvo and S. D. Mello, Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications, IEEE Transactions on Affective Computing, vol.1, issue.1, pp.18-37, 2010.
DOI : 10.1109/T-AFFC.2010.1

A. Camurri, S. Hashimoto, M. Ricchetti, A. Ricci, K. Suzuki et al., Eyesweb : Toward gesture and affect recognition in interactive dance and music systems Pseudoaffective medulliadrenal secretion, Comput. Music J. Am J Physiol, vol.2430, issue.72, pp.57-69283, 1925.

W. B. Cannon, The james-lange theory of emotion : A critical experiment and an alternative theory, Am J Psychol, vol.39, pp.10-124, 1927.

J. Cassell, H. Vilhjalmsson, and T. Bickmore, BEAT, Proceedings of the 28th annual conference on Computer graphics and interactive techniques , SIGGRAPH '01, pp.477-486, 2001.
DOI : 10.1145/383259.383315

G. Chanel, K. Ansari-asl, and T. Pun, Valence-arousal evaluation using physiological signals in an emotion recall paradigm, 2007 IEEE International Conference on Systems, Man and Cybernetics, pp.2662-2667, 2007.
DOI : 10.1109/ICSMC.2007.4413638

G. Chanel, J. J. Kierkels, T. Soleymani, and . Pun, Short-term emotion assessment in a recall paradigm, International Journal of Human-Computer Studies, vol.67, issue.8, pp.67607-627, 2009.
DOI : 10.1016/j.ijhcs.2009.03.005

L. S. Chen, H. Tao, T. S. Huang, T. Miyasato, and R. Nakatsu, Emotion recognition from audiovisual information, 1998 IEEE Second Workshop on Multimedia Signal Processing (Cat. No.98EX175), pp.83-88, 1998.
DOI : 10.1109/MMSP.1998.738917

A. Choi and W. Woo, Physiological Sensing and Feature Extraction for Emotion Recognition by Exploiting Acupuncture Spots, Lecture Notes in Computer Science, vol.3784, pp.590-597, 2005.
DOI : 10.1007/11573548_76

Z. Chuang and C. Wu, Multi-modal emotion recognition from speech and text, Computational Linguistics and Chinese Language Processing, pp.45-62, 2004.

S. Chung, L'expression et la perception de l'émotion extraite de la parole spontanée : évidences du coréen et de l'anglais, p.23, 2000.

I. Cohen, F. Cozman, N. Sebe, M. Cirelo, and T. S. Huang, Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.12, pp.1553-1567, 2004.
DOI : 10.1109/TPAMI.2004.127

R. Cowie, E. Douglas-cowie, N. Tsapatsoulis, G. Votsis, S. Kollias et al., Emotion recognition in human-computer interaction, Signal Processing Magazine, p.54
DOI : 10.1109/79.911197

D. R. Cox, Two further applications of a model for binary regression, Biometrika, vol.45, issue.3-4, pp.562-565, 1958.
DOI : 10.1093/biomet/45.3-4.562

J. Grafman, T. Zalla, and D. Sander, The human amygdala : an evolved system for relevance detection, Rev Neurosci, vol.14, issue.4, pp.303-316, 2003.

A. Damasio and . L-'erreur-de-descartes, La raison des émotions, p.9, 1994.

T. Danisman, Rapport technique, p.105, 2012.

T. Danisman, I. M. Bilasco, N. Ihaddadene, and C. Djeraba, Automatic facial feature detection for facial expression recognition, pp.407-412, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00812308

C. Darwin, The expression of emotion in man and animal, p.165

M. E. Dawson, A. M. Schell, and D. L. Filion, The electrodermal system. Handbook of Psychophysiology second edition Cambridge University, pp.200-223, 2000.

H. B. Deng, L. W. Jin, L. Zhen, and J. C. Huang, A new facial expression recognition method based on local gabor filter bank and pca plus lda, International Journal of Information Technology, vol.11, issue.11, pp.86-96, 2005.

L. Devillers and L. Vidrascu, Real-life emotions detection with lexical and paralinguistic cues on human-human call center dialogs, INTERSPEECH -ICSLP, Ninth International Conference on Spoken Language Processing, p.26, 2006.

P. , E. Sussex, and U. K. , Basic emotions, pp.301-320, 1999.

P. Ekman, Darwin, Deception, and Facial Expression, Annals of the New York Academy of Sciences, vol.20, issue.1, pp.205-221, 2003.
DOI : 10.1196/annals.1280.010

P. Ekman and R. J. Davidson, Voluntary Smiling Changes Regional Brain Activity, Psychological Science, vol.29, issue.5, pp.342-345, 1993.
DOI : 10.1111/j.1467-9280.1992.tb00251.x

P. Ekman and W. V. Friesen, Facial Action Coding System : A Technique for Measurement of Facial Movement, pp.22-167, 1978.

P. Ekman and W. V. Friesen, What emotion categories or dimensions can observers judge from facial behavior, Emotion in the human face, p.16, 1982.

P. Ekman, W. V. Friesen, and P. Ellsworth, Emotion in the human face : guidelines for research and an integration of findings, p.9, 1972.

P. Ekman, R. W. Levenson, and W. V. Friesen, Autonomic nervous system activity distinguishes among emotions, Science, vol.221, issue.4616, pp.1208-1210, 1983.
DOI : 10.1126/science.6612338

K. , R. El, and R. Peter, Real-time inference of complex mental states from facial expressions and head gestures, Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop, pp.154-154, 2004.

I. Essa and A. P. Pentland, Coding, analysis, interpretation, and recognition of facial expressions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.7, p.23
DOI : 10.1109/34.598232

T. Ezzat, G. Geiger, and T. Poggio, Trainable videorealistic speech animation, Appeared in Proceedings of SIGGRAPH. 21, pp.388-398, 2002.
DOI : 10.1145/566570.566594

URL : http://cerboli.mit.edu:8080/publications/siggraph02.ps.gz

M. C. Pfaltz, F. H. Wilhelm, and P. Grossman, Continuous electronic data capture of physiology, behavior and experience in real life : towards ecological momentary assessment of emotion Fasel, F. Monay, and D. Gatica-Perez. Latent semantic analysis of facial action codes for automatic facial expression recognition, Proc. Sixth ACM Int'l Workshop Multimedia Information Retrieval, pp.171-186, 2004.

R. Fernandez and R. W. Picard, Modeling drivers??? speech under stress, Speech Communication, vol.40, issue.1-2, pp.145-159, 1926.
DOI : 10.1016/S0167-6393(02)00080-8

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

C. Féré, Note sur les modifications de la resistance electrique sous l'influence des excitations sensorielles et des emotions, Compt. Rend. Soc. Biol, vol.5, pp.217-219, 1888.

D. George and P. Mallery, SPSS for Windows step by step : A simple guide and reference, p.128, 2003.

W. S. Gosset, The probable error of a mean, pp.1-25, 1908.

J. Gratch, Building emotional agents, Rapport technique cmu-cs-92-143, The fourth international conference on Autonomous agents, p.17, 2000.

J. A. Gray, The neuropsychology of anxiety -an inquiry into the functions of the septo-hippocampal system, p.15, 1982.

P. Greenspan, Emotions & reasons : an inquiry into emotional justification, Routledge, issue.9, 1988.

J. J. Gross and L. F. Barrett, Emotion Generation and Emotion Regulation: One or Two Depends on Your Point of View, Emotion Review, vol.98, issue.1, pp.8-16, 2011.
DOI : 10.1177/1754073910380974

H. Gunes and M. Piccardi, Affect Recognition from Face and Body: Early Fusion vs. Late Fusion, 2005 IEEE International Conference on Systems, Man and Cybernetics, pp.3437-3443, 2005.
DOI : 10.1109/ICSMC.2005.1571679

URL : https://opus.lib.uts.edu.au/bitstream/10453/2745/3/2005003128.pdf

M. Gurban, Multimodal Feature Extraction and Fusion for Audio-visuel Speech Recognition, these doctorale en informatique, communications et information, p.32, 2009.

A. C. Guyton and J. E. Hall, Textbook of medical physiology 11th edition, p.46, 2006.

A. Haag, S. Goronzya, P. Schaich, and J. Williams, Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System, Affective Dialogue Systems, pp.36-48, 2004.
DOI : 10.1007/978-3-540-24842-2_4

H. Hamdi, P. Richard, A. Suteau, and P. Alain, Emotion assessment for affective computing based on physiological responses, 2012 IEEE International Conference on Fuzzy Systems, pp.89-96, 2012.
DOI : 10.1109/FUZZ-IEEE.2012.6250778

URL : https://hal.archives-ouvertes.fr/hal-00846520

Z. Hammal, L. Couvreur, A. Caplier, and M. Rombaut, Facial expressions classification : A new approach based on transferable belief model, International Juornal of Approximate Reasoning, pp.542-567, 2007.

Z. Hammal and C. Massot, Holistic and feature-based information towards dynamic multi-expressions recognition, VISAPP 2010. International Conference on Computer Vision Theory and Applications, pp.300-309, 2010.

B. Hartmann, M. Mancini, and C. Pelachaud, Formational parameters and adaptive prototype instantiation for MPEG-4 compliant gesture synthesis, Proceedings of Computer Animation 2002 (CA 2002), p.27
DOI : 10.1109/CA.2002.1017516

J. Healey and R. W. Picard, SmartCar: detecting driver stress, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, pp.218-221, 2000.
DOI : 10.1109/ICPR.2000.902898

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

J. A. Healey and R. W. Picard, Detecting Stress During Real-World Driving Tasks Using Physiological Sensors, IEEE Transactions on Intelligent Transportation Systems, vol.6, issue.2, pp.156-166, 2005.
DOI : 10.1109/TITS.2005.848368

A. Heraz and C. Frasson, Predicting the three major dimensions of the learner's emotions from brainwaves, World Academy of Science, Eng. and Technology, vol.25, pp.323-329, 2007.

B. Herbelin, P. Benzaki, F. Riquier, O. Renault, and D. Thalmann, Using physiological measures for emotional assessment: A computer-aided tool for cognitive and behavioural therapy, ICDVRAT, pp.307-314, 2004.
DOI : 10.1515/IJDHD.2005.4.4.269

T. S. Huang, L. S. Chen, H. Tao, T. Miyasato, and R. Nakatsu, Bimodal emotion recognition by man and machine, ATR Workshop on Virtual Communication Environments, p.31, 1998.

C. Izard, The face of emotion. Appleton-Century-Crofts, p.118, 1971.

N. J. Kim, J. Wagner, and E. Andre, From physiological signals to emotions : Implementing and comparing selected methods for feature extraction and classification, ICME, pp.940-943, 2005.

A. Jaimes and N. Sebe, Multimodal human???computer interaction: A survey, Computer Vision and Image Understanding, vol.108, issue.1-2, pp.116-134, 2007.
DOI : 10.1016/j.cviu.2006.10.019

W. James, What is emotion? 1884., p.15
DOI : 10.1037/11304-033

H. H. Jasper, The ten-twenty electrode system of the international federation in electroencephalography and clinical neurophysiology, EEG Journal, p.40, 1958.

P. Teissier, P. Escudier, and J. L. Schwartz, Traitement automatique du langage parlé -2 : reconnaissance de la parole, chapitre la parole multimodale, pp.141-178, 2002.

K. Kahler, J. Haber, and H. Seidel, Geometry-based muscle modeling for facial animation, No description on Graphics interface, pp.37-46, 2001.

A. Kapoor, S. Mota, and R. W. Picard, Towards a learning companion that recognizes affect emotional and intelligent ii : The tangled knot of social cognition, p.27, 2001.

K. Karpouzis, A. Raouzaiou, . St, and . Kollias, Moving' avatars : Emotion Synthesis in Virtual Worlds, pp.503-507, 2003.

C. D. Katsis, N. Katertsidis, G. Ganiatsas, and D. I. Fotiadis, Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.38, issue.3, pp.502-512, 2008.
DOI : 10.1109/TSMCA.2008.918624

Z. Khalili and M. H. Moradi, Emotion recognition system using brain and peripheral signals : using correlation dimension to improve the results of eeg Eeg-based emotion recognition using self-organizing map for boundary detection, Proceedings of the 2009 international joint conference on Neural Networks Proceedings of the 2010 20th International Conference on Pattern Recognition, pp.1571-1575, 2009.

J. Kim, Bimodal Emotion Recognition using Speech and Physiological Changes, p.64, 2007.
DOI : 10.5772/4754

J. Kim and E. André, Emotion recognition based on physiological changes in music listening, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.12, pp.2067-2083, 2008.

K. Kim, S. Bang, and S. Kim, Emotion recognition system using short-term monitoring of physiological signals, Medical & Biological Engineering & Computing, vol.10, issue.3, pp.419-427, 2004.
DOI : 10.1007/BF02344719

J. L. Lagrange and L. Poinsot, Traité de la résolution des équations numériques de tous les degrés, p.51, 1806.

R. D. Lane, E. M. Reiman, M. M. Bradley, P. J. Lang, G. L. Ahern et al., euroanatomical correlates of pleasant and unpleasant emotions, neuropsychologia, Neuropsychologia, issue.11, pp.351437-1444, 1997.

P. Lang, M. Bradley, and B. Cuthbert, International affective picture system (iaps) : Affective ratings of pictures and instruction manual, p.62, 2005.

P. J. Lang, M. K. Greenwald, M. M. Bradley, and A. O. Hamm, Looking at pictures: Affective, facial, visceral, and behavioral reactions, Psychophysiology, vol.3, issue.3, pp.261-273, 1993.
DOI : 10.1016/0022-1031(84)90047-7

R. Lazarus, Emotion and Adaptation, p.167, 1991.

R. Lazarus and S. Folkman, Stress, Appraisal, and Coping, 1984.

S. Shrikanth, M. Lee, and . Narayanan, Toward detecting emotions in spoken dialogs, IEEE Transactions on Speech and Audio Processing, vol.13, issue.24, pp.293-303, 2005.

E. Leon, G. Clarke, V. Callaghan, and F. Sepulveda, A user-independent real-time emotion recognition system for software agents in domestic environments, Engineering Applications of Artificial Intelligence, vol.20, issue.3, pp.337-345, 1928.
DOI : 10.1016/j.engappai.2006.06.001

R. W. Levenson, The Autonomic Nervous System and Emotion, Social Psychophysiology and Emotion : Theory and Clinical Applications, pp.17-42, 1988.
DOI : 10.1037/a0017896

C. Lisetti and F. Nasoz, Using Noninvasive Wearable Computers to Recognize Human Emotions from Physiological Signals, EURASIP Journal on Advances in Signal Processing, vol.2004, issue.11, pp.1672-1687, 2004.
DOI : 10.1155/S1110865704406192

C. L. Lisetti and F. Nasoz, MAUI, Proceedings of the tenth ACM international conference on Multimedia , MULTIMEDIA '02, pp.161-170, 2002.
DOI : 10.1145/641007.641038

D. J. Litman and K. Forbes-riley, Predicting student emotions in computerhuman tutoring dialogues, Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, pp.351-358, 2004.

G. C. Littlewort, M. S. Bartlett, and K. Lee, Faces of pain :automated measurement of spontaneous facial expressions of genuine and posed pain, Proc. Ninth ACM Int'l Conf. Multimodal Interfaces (ICMI '07), pp.15-21, 2007.

C. Liu, K. Conn, N. Sarkar, and W. Stone, Physiology-based affect recognition for computer-assisted intervention of children with Autism Spectrum Disorder, International Journal of Human-Computer Studies, vol.66, issue.9, pp.662-677, 1928.
DOI : 10.1016/j.ijhsc.2008.04.003

C. Liu, P. Rani, and N. Sarkar, Human-Robot Interaction Using Affective Cues, ROMAN 2006, The 15th IEEE International Symposium on Robot and Human Interactive Communication, pp.285-290, 2006.
DOI : 10.1109/ROMAN.2006.314431

A. Luneski and P. D. Bamidis, Towards an emotion specification method : Representing emotional physiological signals. Computer-Based Medical Systems, IEEE Symposium on, vol.0, pp.363-370, 2007.
DOI : 10.1109/cbms.2007.113

S. L. Lynn, The Sociology of Affect and Emotion, pp.118-166, 1995.

S. B. Marian, L. Gwen, F. Mark, L. Claudia, F. Ian et al., Fully automatic facial action recognition in spontaneous behavior, Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, pp.223-230, 2006.

S. Marsella and J. Gratch, Modeling the interplay of emotions and plans in multiagent simulations. page 109, 23rd Annual Conference of the Cognitive Science Society, p.17, 2001.

A. Martin, Fusion de classifieurs pour la classification d'images sonar, pp.259-268, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00286591

K. Mase, Recognition of facial expression from optical flow, IEICE transactions, vol.74, issue.10, pp.3473-3483, 1991.

W. Mcdougall, An introduction to social psychology, p.15, 1926.
DOI : 10.1037/12261-000

R. A. Mcfarland, Relationship of skin temperature changes to the emotions accompanying music, Biofeedback and Self-Regulation, vol.23, issue.3, pp.255-267, 1985.
DOI : 10.1007/BF00999346

P. D. Mclean, Some psychiatric implications of physiological studies on frontotemporal portion of limbic system (Visceral brain), Electroencephalography and Clinical Neurophysiology, vol.4, issue.4, pp.407-418, 1952.
DOI : 10.1016/0013-4694(52)90073-4

J. A. Mikels, B. L. Fredrickson, G. R. Larkin, C. M. Lindberg, S. J. Maglio et al., Emotional category data on images from the international affective picture system, Behavior Research Methods, vol.77, issue.4, pp.636-630, 2005.
DOI : 10.3758/BF03192732

J. S. Morris, C. D. Frith, D. I. Perrett, D. Rowland, A. W. Young et al., A differential neural response in the human amygdala to fearful and happy facial expressions, Nature, vol.383, issue.6603, pp.812-815, 1996.
DOI : 10.1038/383812a0

O. H. Mower, Learning theory and behavior, p.15, 1960.

O. H. Mowrer, Learning theory and behavior, p.14, 1960.
DOI : 10.1037/10802-000

C. Muhl, A. Brouwer, N. Van-wouwe, E. L. Van-den-broek, F. Nijboer et al., Modality-specific affective responses and their implications for affective bci, Proceedings of the Fifth International Brain-Computer Interface Conference 2011, pp.120-123, 2011.

C. Muhl, E. L. Van-den-broek, A. Brouwer, F. Nijboer, N. Wouwe et al., Multi-modal Affect Induction for Affective Brain-Computer Interfaces, Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction, pp.235-245, 2011.
DOI : 10.1109/TPAMI.2008.52

R. I. Murray and J. L. Arnott, Synthesizing emotions in speech: is it time to get excited?, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96, pp.1816-1819, 1996.
DOI : 10.1109/ICSLP.1996.607983

F. Nasoz, K. Alvarez, L. Lisetti, and N. Finkelstein, Emotion recognition from physiological signals using wireless sensors for presence technologies, Cognition, Technology & Work, vol.6, issue.1, pp.4-14, 1928.
DOI : 10.1007/s10111-003-0143-x

D. Nie, X. W. Wang, L. C. Shi, and B. L. Lu, Eeg-based emotion recognition during watching movie, IEEE EMBS Conference on Neural Engineering, pp.667-670, 2011.
DOI : 10.1109/ner.2011.5910636

URL : http://bcmi.sjtu.edu.cn/%7Eblu/papers/2011/EEG-Based+Emotion+Recognition+During+Watching+Movies.pdf

L. Nigay and J. Coutaz, Espaces conceptuels pour l'interaction multimédia et multimodale. TSI, spécial Multimédia et Collecticiel, pp.1195-1225, 1996.

A. Schaefer, F. Nils, X. Sanchez, and P. Philippot, A multi-criteria assessment of emotional films, pp.56-74, 2007.

K. Oatley and P. N. Johnson-laird, Towards a cognitive theory of emotions. cognition & emotion, p.118, 1987.

A. Ortony, G. Clore, and A. Collins, The cognitive structure of emotions, p.167, 1988.
DOI : 10.1017/CBO9780511571299

A. Ortony and W. Turner, What's basic about basic emotions?, Psychological Review, pp.315-331, 1990.
DOI : 10.1037/0033-295X.97.3.315

N. Naqvi, P. Rainville, A. Bechara, and A. R. Damasio, Basic emotions are associated with distinct patterns of cardiorespiratory activity, International Journal of Psychophysiology, vol.61, issue.46, pp.5-18, 2006.

J. L. Armony, J. Driver, R. J. Dolan, P. Vuilleumier, and M. P. Richardson, Distant influences of amygdala lesion on visual cortical activation during emotional face processing, Nat Neurosci, vol.7, issue.11, pp.1271-1278, 2004.

M. Paleari, B. Huet, and B. Duffy, SAMMI, Proceedings of the semantic and digital media technologies 2nd international conference on Semantic Multimedia, pp.300-303, 2007.
DOI : 10.1007/978-3-540-77051-0_39

M. Paleari and C. L. Lisetti, Toward multimodal fusion of affective cues, Proceedings of the 1st ACM international workshop on Human-centered multimedia , HCM '06, pp.99-108, 2006.
DOI : 10.1145/1178745.1178762

J. Panksepp, Toward a general psychobiological theory of emotions, Behavioral and Brain Sciences, vol.36, issue.03, pp.407-422, 1982.
DOI : 10.1159/000125871

M. Pantic and I. Patras, Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.36, issue.2, pp.433-449, 2006.
DOI : 10.1109/TSMCB.2005.859075

M. Pantic and L. J. Rothkrantz, Toward an affect-sensitive multimodal humancomputer interaction, Proceedings of the IEEE, pp.1370-1390, 2003.
DOI : 10.1109/jproc.2003.817122

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

M. Pantic, N. Sebe, J. F. Cohn, and T. Huang, Affective multimodal humancomputer interaction, ACM Int'l Conf. on Multimedia 2005, pp.669-676, 2005.
DOI : 10.1145/1101149.1101299

F. Parke, A parametric model for human faces. page 109. The University of Utah, 1921.

S. Pasquariello and C. Pelachaud, Greta: A Simple Facial Animation Engine, Proceedings of the 6th Online World Conference on Soft Computing in Industrial Applications, Session on Soft Computing for Intelligent 3D Agents, p.20, 2001.
DOI : 10.1007/978-1-4471-0123-9_43

P. C. Petrantonakis and L. J. Hadjileontiadis, Emotion Recognition From EEG Using Higher Order Crossings, IEEE Transactions on Information Technology in Biomedicine, vol.14, issue.2, pp.186-197, 2010.
DOI : 10.1109/TITB.2009.2034649

R. Pfeifer, S. Kaiser, and T. Wehrle, Artificial Intelligence Models of Emotion, Cognitive Perspectives on Emotion and Motivation, pp.287-320, 1988.
DOI : 10.1007/978-94-009-2792-6_12

R. W. Picard, Affective Computing, rapport interne du MIT Media Lab. Massachusetts Institute of Technology, pp.13-167, 1997.

R. W. Picard, E. Vyzas, and J. Healey, Toward machine emotional intelligence: analysis of affective physiological state, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.10, pp.1175-1191, 2001.
DOI : 10.1109/34.954607

R. Plutchik, A general psychoevolutionary theory of emotion In Emotion : Theory, research, and experience, pp.3-33, 1980.

F. Provost and T. Fawcett, Analysis and visualisation of classifier performance : Comparison under imprecise class and cost distributions, Proc. third internat. Conf. on Knowledge Discovery and Data Mining, KDD-97, pp.43-48, 1997.

F. Provost and T. Fawcett, Robust classification systems for imprecise environments, Proc. AAAI-98, pp.706-713, 1998.

D. Purves, G. J. Augustine, D. Fitzpatrick, L. C. Katz, and . Neuroscience, Traduction de la 1ère édition américaine par j.-m. coquery edition, p.46, 1997.

P. Rani, C. Liu, N. Sarkar, and E. Vanman, An empirical study of machine learning techniques for affect recognition in human???robot interaction, Pattern Analysis and Applications, vol.13, issue.4, pp.58-69, 2006.
DOI : 10.1007/s10044-006-0025-y

W. S. Reilly and J. Bates, Building emotional agents, Rapport technique cmu-cs- 92-143, p.17, 1992.

D. Rene, The Passions of the Soul, pp.325-404

L. M. Romanski and J. E. Ledoux, Equipotentiality of thalamo-amygdala and thalamocortico-amygdala circuits in auditory fear conditioning, J Neurosci, vol.12, issue.11, pp.4501-4509, 1992.

I. J. Roseman, Cognitive aspects of emotion and emotional behavior, 87th Annual Convention of the American Psychological Association, p.18, 1979.

I. J. Roseman, Appraisal Determinants of Emotions: Constructing a More Accurate and Comprehensive Theory, Cognition & Emotion, vol.10, issue.3, pp.241-278, 1996.
DOI : 10.1080/026999396380240

J. Rottenberg, R. D. Ray, and J. J. Gross, Emotion elicitation using films. ser. Series in affective science, pp.9-28, 2007.

D. Roy and A. Pentland, Automatic spoken affect classification and analysis. automatic face and gesture recognition, Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96), pp.363-367, 1996.
DOI : 10.1109/afgr.1996.557292

A. Russell, Affective space is bipolar., Journal of Personality and Social Psychology, vol.37, issue.3, pp.345-356, 1979.
DOI : 10.1037/0022-3514.37.3.345

S. Saint-aimé, Conception et réalisation d'un robot compagnon expressif basé sur un modèle calculatoire des émotions, 2008.

T. Sakata, S. Watanuki, H. Sakamoto, T. Sumi, and Y. K. Kim, Objective evaluation of kansei by a complementary use of physiological indexes, brain wave and facial xpressions for user oriented designs, Proceedings of the10th Qmod conference, Quality Management and Organisational Development : Our Dreams of Excellence, p.78, 2007.

G. Saporta, Probabilités, analyse de données et statistique, p.51, 1990.

S. Schachter and J. E. Singer, Cognitive, social, and physiological determinants of emotional state, pp.379-399, 1962.

K. R. Scherer, The nature and function of emotion, Social Science Information, vol.21, issue.4-5, pp.293-317, 1984.
DOI : 10.1177/053901882021004001

K. R. Scherer, What are emotions? And how can they be measured?, Social Science Information, vol.42, issue.1, pp.695-729, 2005.
DOI : 10.1177/0539018405058216

K. R. Scherer, The component process model : a blueprint for a comprehensive computational model of emotion, p.167, 2010.

K. R. Scherer, Emotion. in Introduction to Social Psychology : A European perspective, pp.151-191, 2000.
URL : https://hal.archives-ouvertes.fr/hal-01494132

K. R. Scherer, Vocal communication of emotion: A review of research paradigms, Speech Communication, vol.40, issue.1-2, pp.227-256, 2003.
DOI : 10.1016/S0167-6393(02)00084-5

B. Schuller, G. Rigoll, and M. Lang, Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.577-80, 1924.
DOI : 10.1109/ICASSP.2004.1326051

B. Schuller, J. Stadermann, and G. , Affect-robust speech recognition by dynamic emotional adaptation, Invited for Proc. Speech Prosody 2006, Special Session Prosody in Automatic Speech Recognition, p.26, 2006.

B. Schuller, R. J. Villar, G. Rigoll, and M. Lang, Meta-Classifiers in Acoustic and Linguistic Feature Fusion-Based Affect Recognition, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., pp.325-328, 2005.
DOI : 10.1109/ICASSP.2005.1415116

F. Sharbrough, G. E. Chatrian, R. P. Lesser, H. Ludersand, M. Nu-wer et al., American electroencephalographic society guidelines for standard electrode position nomenclature, J. Clin. Neurophysiol, vol.8, issue.41, pp.200-202, 1991.

R. Sharma, V. I. Pavlovic, and T. S. Huang, Toward multimodal human-computer interface. roceedings of the IEEE, pp.853-869, 1998.
DOI : 10.1109/5.664275

S. A. Shields, From the Heart : Gender and the Social Meaning of Emotion, p.78, 2002.

B. G. Silverman, More realistic human behavior models for agents in virtual worlds : Emotion, stress, and value ontologies, p.17, 2001.

R. W. Simon and E. N. Leda, Gender and Emotion in the United States: Do Men and Women Differ in Self???Reports of Feelings and Expressive Behavior?, American Journal of Sociology, vol.109, issue.5, pp.1137-76, 2004.
DOI : 10.1086/382111

A. Sloman and M. Croucher, Why robots will have emotions, Originally appeared in Proceedings IJCAI 1981, 1981.

M. Soleymani, J. Lichtenauer, T. Pun, and M. Pantic, A multi-modal affective database for affect recognition and implicit tagging, IEEE Transactions on Affective Computing, Special Issue on Naturalistic Affect Resources for System Building and Evaluation, vol.1, issue.99, 1928.

P. Somol, P. Pudil, J. Novovicová, and P. Paclík, Adaptive floating search methods in feature selection, Pattern Recognition Letters, vol.20, issue.11-13, pp.11-131157, 1999.
DOI : 10.1016/S0167-8655(99)00083-5

R. Sprengelmeyer, M. Rausch, U. T. Eysel, and H. Przuntek, Neural structures associated with recognition of facial expressions of basic emotions, Proceedings of the Royal Society of London. Series B, pp.1927-1931, 1998.
DOI : 10.1098/rspb.1998.0522

G. Stemmler, M. Heldmann, C. A. Pauls, and T. Scherer, Constraints for emotion specificity in fear and anger: The context counts, Psychophysiology, vol.38, issue.2, pp.275-291, 2001.
DOI : 10.1111/1469-8986.3820275

G. M. Stratton, Cattle, and Excitement from Blood., Psychological Review, vol.30, issue.5, pp.380-387, 1923.
DOI : 10.1037/h0074723

A. B. Dubois, H. Folgering, G. K. Fritz, A. Harver, H. Kotses et al., Guidelines for mechanical lung function measurements in psychophysiology, Psychophysiology, vol.39, issue.46, pp.546-567, 2002.

K. Takahashi, Remarks on emotion recognition from bio-potential signals, 2nd Int. Conf. Autonomous Robots Agents, pp.186-191, 2004.

P. A. Thoits, The Sociology of Emotions, Annual Review of Sociology, vol.15, issue.1, pp.837-57, 1989.
DOI : 10.1146/annurev.so.15.080189.001533

Y. Tian, T. Kanade, J. S. Cohn-]-s, and . Tomkins, Recognizing lower face action units for facial expression analysis Affect Imagery Consciousness : Volume I, The Positive Affect, Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition, pp.484-490, 1962.

S. S. Tomkins, Approaches to emotion, chapter Affect Theory, pp.163-195, 1984.

Y. Tong, W. Liao, and Q. Ji, Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.10, pp.1683-1699, 2007.
DOI : 10.1109/TPAMI.2007.1094

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

P. Khiet, D. A. Truong, and . Van-leeuwen, Automatic discrimination between laughter and speech, Speech Commun, vol.49, issue.2, pp.144-158, 1926.

M. F. Valstar, H. Gunes, and M. Pantic, How to distinguish posed from spontaneous smiles using geometric features, Proceedings of the ninth international conference on Multimodal interfaces , ICMI '07, pp.38-45, 2007.
DOI : 10.1145/1322192.1322202

L. Egon, J. H. Van-den-broek, and . Westerink, Guidelines for affective signal processing (asp) : from lab to life, Proceedings of the 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009, pp.704-709, 2009.

V. N. Vapnik, An overview of statistical learning theory, IEEE Transactions on Neural Networks, vol.10, issue.5, pp.988-999, 1999.
DOI : 10.1109/72.788640

H. Vilhjalmsson, N. Cantelmo, J. Cassell, N. E. Chafai, M. Kipp et al., Werf van der. The behavior markup language : Recent developments and challenges, Intelligent Virtual Agents, pp.99-111, 2007.

O. Villon, Modeling affective evaluation of multimedia contents : user models to associate subjective experience, physiological expression and contents description, 1927.

P. Vuilleumier, How brains beware: neural mechanisms of emotional attention, Trends in Cognitive Sciences, vol.9, issue.12, pp.585-594, 2005.
DOI : 10.1016/j.tics.2005.10.011

E. Vyzas and R. W. Picard, Affective pattern classification Series : Emotional and Intelligent : The Tangled Knot of Cognition, Proc. AAAI Fall Symp, pp.176-182, 1998.

J. Wagner, E. Andre, and F. Jung, Smart sensor integration: A framework for multimodal emotion recognition in real-time, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, pp.1-8, 2009.
DOI : 10.1109/ACII.2009.5349571

H. Wang, F. Azuaje, B. Jung, and N. Black, A markup language for electrocardiogram data acquisition and analysis (ecgML), BMC Medical Informatics and Decision Making, vol.5, issue.3, pp.4-89, 2003.
DOI : 10.1109/4236.935177

J. Wang, L. Yin, X. Wei, and Y. Sun, 3d facial expression recognition based on primitive surface feature distribution, Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition (CVPR'06), pp.1399-1406, 2006.

D. Watson and L. A. Clark, On Traits and Temperament: General and Specific Factors of Emotional Experience and Their Relation to the Five-Factor Model, Journal of Personality, vol.1, issue.2, pp.441-476, 1992.
DOI : 10.1037//0022-3514.43.1.111

D. Westen, Pensée, cerveau et culture, 2000.

M. Wolff, Apports de l'analyse géométrique des données pour la modélisation de l'activité Formalismes de modélisation pour l'analyse du travail et l'ergonomie, pp.195-227, 2003.

M. Wolff and W. Visser, M??todos y herramientas para el an??lisis de verbalizaciones??: una contribuci??n al an??lisis del modelo del interlocutor en la descripci??n de itinerarios, Activités, pp.99-118, 2005.
DOI : 10.4000/activites.1612

L. Xiangyang, J. Qiang, and M. Senior, Active affective state detection and user assistance with dynamic bayesian networks, Transactions on Systems, Man , and Cybernetics -Part A :Systems and Humans, pp.93-105, 2005.

Y. Yacoob and L. Davis, Computing spatio-temporal representations of human faces, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94, pp.70-75, 1994.
DOI : 10.1109/CVPR.1994.323812

G. N. Yannakakis and J. Hallam, Entertainment modeling through physiology in physical play, International Journal of Human-Computer Studies, vol.66, issue.10, pp.741-755, 2008.
DOI : 10.1016/j.ijhcs.2008.06.004

S. K. Yoo, C. K. Lee, J. Y. Park, N. H. Kim, B. C. Lee et al., Neural Network Based Emotion Estimation Using Heart Rate Variability and Skin Resistance, Lecture Notes in Computer Science (Advances in Natural Computation), vol.3612, issue.28, pp.818-824, 2005.
DOI : 10.1007/11539087_110

Y. Yoshitomi, S. Kim, T. Kawano, and T. Kilazoe, Effect of sensor fusion for recognition of emotional states using voice, face image and thermal image of face, Proceedings 9th IEEE International Workshop on Robot and Human Interactive Communication. IEEE RO-MAN 2000 (Cat. No.00TH8499), pp.178-183, 2000.
DOI : 10.1109/ROMAN.2000.892491

C. Yu, P. M. Aoki, and A. Woodruff, Detecting user engagement in everyday conversations, Proceedings of 8 th International Conference on Spoken Language Processing, pp.1329-1332, 2004.

Z. Zeng, Y. Hu, M. Liu, Y. Fu, and T. S. Huang, Training combination strategy of multi-stream fused hidden Markov model for audio-visual affect recognition, Proceedings of the 14th annual ACM international conference on Multimedia , MULTIMEDIA '06, pp.65-68, 2006.
DOI : 10.1145/1180639.1180661

Z. Zeng, J. Tu, M. Lu, T. S. Huang, B. Pianfetti et al., Audio-Visual Affect Recognition, IEEE Transactions on Multimedia, vol.9, issue.2, pp.424-466, 2007.
DOI : 10.1109/TMM.2006.886310

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

Z. Zeng, M. Pantic, G. I. Roisman, and T. S. Huang, A survey of affect recognition methods, Proceedings of the ninth international conference on Multimodal interfaces , ICMI '07, pp.39-58, 2009.
DOI : 10.1145/1322192.1322216

J. Zhai and A. Barreto, Stress detection in computer users through noninvasive monitoring of physiological signals, Biomedical Science Instrumentation, vol.42, pp.495-500, 2006.

H. Zhang, The optimality of naive bayes, Proceedings of the Seventeenth Florida Artificial Intelligence Research Society Conference, pp.562-567, 2004.

.. Systèmes-de-reconnaissance-d-'émotions, 20 2.3.1 Introduction

. Acquisition-et-traitement-de-signaux-physiologiques........, 47 3.3.1 Extraction et caractérisation de l'activité physiologique, p.47

.. Évaluation-de-l-'émotion-via-les-signaux-physiologiques, 53 3.4.1 Critères d'évaluations, 53 3.4.2 Techniques d'induction standardisées . . . . . . . . . . . . . . . 54

.. Extraction-des-modèles-mathématiques, 82 4.7.1 Description des modèles, p.83

]. Métaphore-de-la-cloche, (a) : stimuli émotionnels de différentes intensités . (b) : réponses émotionnelles résultantes, et (c) : somme des réponses émotionnelles [151, p.13

'. Exemples-d, Action Units [56, p.23

.. Gestionnaire-d-'émissions-Émissions-réactives, 118 6.11 Architecture de l'agrégateur, p.120

.. Epoc, Exemple de fichier XML correspondant aux expressions faciales identifies via la suite affective de l, p.141