A. Appriou, Multisensor signal processing in the framework of the theory of evidence In Application of mathematical signal processing techniques to mission systems, Research and Technology Organisation (lecture series 216, pp.5-6, 1999.

M. Arulampalam, S. Maskell, G. N. Clapp, and T. , A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing, vol.50, issue.2, pp.174-188, 2002.
DOI : 10.1109/78.978374

B. Babenko, M. Yang, and S. Belongie, Robust Object Tracking with Online Multiple Instance Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.8, pp.1619-1632, 2011.
DOI : 10.1109/TPAMI.2010.226

M. Castrillón, O. Déniz, D. Hernández, and J. Lorenzo, A comparison of face and facial feature detectors based on the Viola???Jones general object detection framework, Machine Vision and Applications, pp.481-494, 2011.
DOI : 10.1007/s00138-010-0250-7

D. Comaniciu, V. Ramesh, and P. Meer, Kernel-based object tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.5, pp.564-575, 2003.
DOI : 10.1109/TPAMI.2003.1195991

T. F. Cootes, G. J. Edwards, and C. J. Taylor, Active appearance models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.6, pp.681-685, 2001.
DOI : 10.1109/34.927467

T. F. Cootes and C. J. Taylor, Active shape models -'smart snakes, Proceedings of British machine vision conference, pp.266-275, 1992.
DOI : 10.5244/c.6.28

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.3986

F. C. Crétual and P. Bouthemy, Complex object tracking by visual servoing based on 2D image motion, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), pp.1251-1254, 1998.
DOI : 10.1109/ICPR.1998.711927

A. Dempster, Upper and Lower Probabilities Induced by a Multivalued Mapping, The Annals of Mathematical Statistics, vol.38, issue.2, pp.325-339, 1967.
DOI : 10.1214/aoms/1177698950

T. Denoeux, A k-nearest neighbor classification rule based on Dempster-Shafer theory, IEEE Transactions on Systems, Man, and Cybernetics, vol.25, issue.5, pp.804-813, 1995.
DOI : 10.1109/21.376493

T. Denoeux, Analysis of evidence-theoretic decision rules for pattern classification, Pattern Recognition, vol.30, issue.7, pp.1095-1107, 1997.
DOI : 10.1016/S0031-3203(96)00137-9

T. Denoeux, Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence, Artificial Intelligence, vol.172, issue.2-3, pp.234-264, 2008.
DOI : 10.1016/j.artint.2007.05.008

T. Denoeux and P. Smet, Classification Using Belief Functions: Relationship Between Case-Based and Model-Based Approaches, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.36, issue.6, pp.1395-1406, 2006.
DOI : 10.1109/TSMCB.2006.877795

F. Faux, Détection et suivi de visage par la théorie de l'évidence, Thèse de doctorat non publiée, 2009.

F. Faux and F. Luthon, Robust face tracking using colour Dempster-Shafer fusion and particle filter, 2006 9th International Conference on Information Fusion, pp.1-7, 2006.
DOI : 10.1109/ICIF.2006.301713

URL : https://hal.archives-ouvertes.fr/hal-00405766

A. Fitzgibbon, M. Pilu, and R. Fisher, Direct least square fitting of ellipses, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.21, issue.5, pp.476-480, 1999.
DOI : 10.1109/34.765658

M. C. Florea, A. Jousselme, E. Bossé, and D. Grenier, Robust combination rules for evidence theory, Information Fusion, vol.10, issue.2, pp.183-197, 2009.
DOI : 10.1016/j.inffus.2008.08.007

Z. Hammal, L. Couvreur, A. Caplier, and M. Rombaut, Facial expression classification: An approach based on the fusion of facial deformations using the transferable belief model, International Journal of Approximate Reasoning, vol.46, issue.3, pp.542-567, 2007.
DOI : 10.1016/j.ijar.2007.02.003

URL : https://hal.archives-ouvertes.fr/hal-00158427

E. Hjelmås and B. Low, Face Detection: A Survey, Computer Vision and Image Understanding, vol.83, issue.3, pp.236-274, 2001.
DOI : 10.1006/cviu.2001.0921

L. Huang, A. Shimizu, and H. Kobatake, Robust face detection using Gabor filter features, Pattern Recognition Letters, vol.26, issue.11, pp.1641-1649, 2005.
DOI : 10.1016/j.patrec.2005.01.015

M. Isard and J. Maccormick, BraMBLe: a Bayesian multiple-blob tracker, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pp.34-41, 2001.
DOI : 10.1109/ICCV.2001.937594

A. Kallel, L. Hégarat-mascle, and S. , Combination of partially non-distinct beliefs: The cautious-adaptive rule, International Journal of Approximate Reasoning, vol.50, issue.7, pp.1000-1021, 2009.
DOI : 10.1016/j.ijar.2009.03.006

J. Klein, C. Lecomte, and P. Miché, Hierarchical and conditional combination of belief functions induced by visual tracking, International Journal of Approximate Reasoning, vol.51, issue.4, pp.410-428, 2010.
DOI : 10.1016/j.ijar.2009.12.001

URL : https://hal.archives-ouvertes.fr/hal-00595038

R. Knothe, B. Amberg, S. Romdhani, V. Blanz, and T. Vetter, Handbook of face recognition, Chapter Morphable models of faces, 2011.

M. Liévin and F. Luthon, Nonlinear Color Space and Spatiotemporal MRF for Hierarchical Segmentation of Face Features in Video, IEEE Transactions on Image Processing, vol.13, issue.1, pp.63-71, 2004.
DOI : 10.1109/TIP.2003.818013

F. Luthon and B. Beaumesnil, Color and r.o.i. with jpeg2000 for wireless videosurveillance, 2004 International Conference on Image Processing, 2004. ICIP '04., pp.3205-3208, 2004.
DOI : 10.1109/ICIP.2004.1421795

URL : https://hal.archives-ouvertes.fr/hal-00787164

F. Luthon, B. Beaumesnil, and N. Dubois, LUX color transform for mosaic image rendering, 2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), pp.93-98, 2010.
DOI : 10.1109/AQTR.2010.5520671

URL : https://hal.archives-ouvertes.fr/hal-00786040

A. Martin and C. Osswald, Towards a combinaison rule to deal with partial conflict and specificity in belief functions theory, International conference on information fusion, pp.9-12, 2007.

S. Phung, A. Bouzerdoum, and D. Chai, Skin segmentation using color pixel classification: analysis and comparison, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.1, pp.148-154, 2005.
DOI : 10.1109/TPAMI.2005.17

URL : http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1254&context=infopapers

F. Pichon and T. Denoeux, Interpretation and computation of alpha-junctions for combining belief functions, 6th international symposium on imprecise probability: Theories and applications (ISIPTA '09), pp.1-10, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00450984

P. Pérez, J. Vermaak, and A. Blake, Data Fusion for Visual Tracking With Particles, Proceedings of the IEEE, vol.92, issue.3, pp.495-513, 2004.
DOI : 10.1109/JPROC.2003.823147

E. Ramasso, C. Panagiotakis, M. Rombaut, and D. Pellerin, Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition, International Journal of Approximate Reasoning, vol.51, issue.7, pp.846-865, 2010.
DOI : 10.1016/j.ijar.2010.04.005

URL : https://hal.archives-ouvertes.fr/hal-00475787

Y. Rathi, N. Vaswani, A. Tannenbaum, and A. Yezzi, Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.8, pp.1470-1475, 2007.
DOI : 10.1109/TPAMI.2007.1081

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3663080

T. Sakai, M. Nagao, and S. Fujibayashi, Line extraction and pattern detection in a photograph, Pattern Recognition, vol.1, issue.3, pp.233-248, 1969.
DOI : 10.1016/0031-3203(69)90006-5

G. Shafer, A mathematical theory of evidence, NJ, 1976.

L. Sigal, S. Sclaroff, and V. Athitsos, Skin color-based video segmentation under time-varying illumination, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.7, pp.862-877, 2004.
DOI : 10.1109/TPAMI.2004.35

F. Smarandache and J. Dezert, Advances and applications of DSmT for information fusion, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01080187

P. Smets, Bayes' theorem generalized for belief functions, European Conference on Artificial Intelligence (ECAI'86), pp.169-171, 1986.

P. Smets, The combination of evidence in the transferable belief model, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.5, pp.447-458, 1990.
DOI : 10.1109/34.55104

P. Smets, Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem, International Journal of Approximate Reasoning, vol.9, issue.1, pp.1-35, 1993.
DOI : 10.1016/0888-613X(93)90005-X

P. Smets, The canonical decomposition of a weighted belief, International joint conference on artificial inteligence, pp.1896-1901, 1995.

P. Smets and R. Kennes, The transferable belief model, Artificial Intelligence, vol.66, issue.2, pp.191-234, 1994.
DOI : 10.1016/0004-3702(94)90026-4

URL : https://hal.archives-ouvertes.fr/hal-01185821

M. Soriano, B. Martinkauppi, S. Huovinen, and M. Laaksonen, Adaptive skin color modeling using the skin locus for selecting training pixels, Pattern Recognition, vol.36, issue.3, pp.681-690, 2003.
DOI : 10.1016/S0031-3203(02)00089-4

V. Vezhnevets, V. Sazonov, and A. Andreeva, A survey on pixel based skin color detection techniques, Proceedings of Graphicon, pp.85-92, 2003.

P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, pp.511-518, 2001.
DOI : 10.1109/CVPR.2001.990517

P. Viola and M. Jones, Fast multi-view face detection, 2003.

B. B. Yaghlane, P. Smets, and K. Mellouli, Independence Concepts for Belief Functions, 8th International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU), pp.357-364, 2000.
DOI : 10.1007/978-3-7908-1796-6_4

M. Yang, D. Kriegman, and N. Ahuja, Detecting faces in images: A survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.1, pp.35-58, 2002.

A. Yilmaz, O. Javed, and M. Shah, Object tracking, ACM Computing Surveys, vol.38, issue.4, pp.1-45, 2006.
DOI : 10.1145/1177352.1177355

W. Zheng and S. M. Bhandarkar, Face detection and tracking using a Boosted Adaptive Particle Filter, Journal of Visual Communication and Image Representation, vol.20, issue.1, pp.9-27, 2009.
DOI : 10.1016/j.jvcir.2008.09.001

L. Zouhal and T. Denoeux, An evidence-theoretic k-NN rule with parameter optimization, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.28, issue.2, pp.263-271, 1998.
DOI : 10.1109/5326.669565