By Francisco José Perales, Josef Kittler
This publication constitutes the refereed complaints of the ninth overseas convention on Articulated movement and Deformable gadgets, AMDO 2016, held in Palma de Mallorca, Spain, in July 2016.The 20 papers offered have been rigorously reviewed and chosen from 34 submissions. The convention handled the subsequent subject matters: complicated special effects and immersive videogames; human modeling and animation; human movement research and monitoring; 3D human reconstruction and popularity; multimodal person interplay and functions; ubiquitous and social computing; layout instruments; enter know-how; programming person interfaces; 3D clinical deformable types and visualization; deep studying equipment for desktop imaginative and prescient and photos; multibiometric.
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Additional resources for Articulated Motion and Deformable Objects: 9th International Conference, AMDO 2016, Palma de Mallorca, Spain, July 13-15, 2016, Proceedings
J. Perales and J. ): AMDO 2016, LNCS 9756, pp. 34–43, 2016. 1007/978-3-319-41778-3 4 Spatiotemporal Facial Super-Pixels for Pain Detection 2 35 Related Work The current systems on pain detection can be divided into two groups: the ﬁrst group only decides if a given image is of a painful case or not, while the second group besides clarifying the pain presence determines its level as well. In the ﬁrst group, the work of [3,14] use a Support Vector Machine (SVM) classiﬁer that works with eigenfaces to see if there is any sign of pain in a given face of an infant or not.
ECCV 2010, Part VI. LNCS, vol. 6316, pp. 518–531. Springer, Heidelberg (2010) 4. : Random forests. Mach. Learn. 45(1), 5–32 (2001) 5. : Decision forests for classiﬁcation, regression, density estimation, manifold learning and semi-supervised learning. Tech. Rep. MSR-TR-2011-114, Microsoft Research (2011) Head-Pose Estimation In-the-Wild Using a Random Forest 33 6. : Real-time facial feature detection using conditional regression forests. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR) (2012) 7.
54 % of the cases the error in our approach is lower than 15◦ . All three approaches with better results use holistic HOG-based face features [10,12,13]. In this constrained context, this global feature is slightly more informative for estimating face pose than the set of local patches that we use in our approach. However, as we show in the sequel, local representations will have better performance in unconstrained situations. Table 1. Head-pose estimation results in a constrained database. 5◦ Haj et al.