Cristinacce D, Cootes T. Feature detection and tracking with constrained local models. In: Proceedings of the British Machine Vision Conference. Edinburgh, UK: BMVA, 2006. 929-938
[2]
Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-577
[3]
Nguyen H, Smeulders A. Robust tracking using foreground-background texture discrimination. International Journal of Computer Vision, 2006, 69(3): 277-293
[4]
Maggio E, Cavallaro A. Accurate appearance-based Bayesian tracking for maneuvering targets. Computer Vision and Image Understanding, 2009, 113(4): 544-555
[5]
Husain M, Saber E, Misic V, Joralemon S P. Dynamic object tracking by partial shape matching for video surveillance applications. In: Proceedings of the IEEE International Conference on Image Processing. Atlanta, USA: IEEE, 2006. 2405-2408
[6]
Jepson A, Fleet D, El-Maraghi T. Robust online appearance models for visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(10): 1296-1311
[7]
Zhou S K, Chellappa R, Moghaddam B. Visual tracking and recognition using appearance-adaptive models in particle filters. IEEE Transactions on Image Processing, 2004, 13(11): 1491-1506
[8]
Yuille A, Hallinan P, Cohen D. Feature extraction from faces using deformable templates. International Journal of Computer Vision, 1992, 8(2): 99-111
[9]
Cootes T F, Taylor C J, Cooper D H, Graham J. Active shape models — their training and application. Computer Vision and Image Understanding, 1995, 61(1): 38-59
[10]
Matthews I, Baker S. Active appearance models revisited. International Journal of Computer Vision, 2004, 60(2): 135-164
[11]
Cootes T F, Walker K, Taylor C J. View-based active appearance models. In: Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition. Grenoble, France: IEEE, 2000. 227-232
[12]
Butakoff C, Frangi A F. Multi-view face segmentation using fusion of statistical shape and appearance models. Computer Vision and Image Understanding, 2010, 114(3): 311-321
[13]
Xiao J, Baker S, Matthews I, Kanade T. Real-time combined 2D+3D active appearance models. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington D.C., USA: IEEE, 2004. 535-542
[14]
Liao W K, Fidaleo D, Medioni G. Integrating multiple visual cues for robust real-time 3D face tracking. In: Proceedings of the 3rd International Conference on Analysis and Modeling of Faces and Gestures. Rio de Janeiro, Brazil: Springer, 2007. 109-123
[15]
Liu X. Video-based face model fitting using adaptive active appearance model. Image and Vision Computing, 2010, 28(7): 1162-1172
[16]
Zhou M, Liang L, Sun J, Wang Y. AAM based face tracking with temporal matching and face segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010. 701-708
[17]
Ma Y, Ding X Q, Wang Z E, Wang N. Robust precise eye location under probabilistic framework. In: Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition. Seoul, Korea: IEEE, 2004. 339-344
[18]
Sung J, Kim D. Adaptive active appearance model with incremental learning. Pattern Recognition Letters, 2009, 30(4): 359-367
[19]
Hall P, Marshall D, Martin R. Incremental eigenanalysis for classification. In: Proceedings of the British Machine Vision Conference. Southampton, UK: BMVA, 1998. 286-295
[20]
Breiman L. Random forests. Machine Learning, 2001, 45(1): 5-32
[21]
Gao W, Cao B, Shan S G, Chen X L, Zhou D, Zhang X H, Zhao D B. The CAS-PEAL large-scale Chinese face database and baseline evaluations. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 2008, 38(1): 149-161
[22]
Huang C, Ding X Q, Fang C. Head pose estimation based on random forests for multiclass classification. In: Proceedings of the 20th International Conference on Pattern Recognition. Istanbul, Turkey: IEEE, 2010. 934-937
[23]
La Cascia M, Sclaroff S, Athitsos V. Fast, reliable head tracking under varying illumination: an approach based on registration of texture-mapped 3D models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(4): 322-336
