Algorithms capable of detecting and tracking landmarks on human faces have shown some level of robustness in handling different variations caused by illumination, pose and expression changes, and partial occlusions.
Facial Deformable Models of Animals (FDMA) project aims to challenge the current state-of-the-art in human facial landmark detection and tracking, and propose new algorithms that can cope with much larger variability, which is typically observed in animal faces.

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FDMA sets out to challenge the current state-of-the-art in detecting and tracking facial landmarks on humans, and develop new algorithms that can handle much larger variation typically seen in animal faces.

 

 

Project Team

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       Georgios Tzimiropoulos        M. Haris Khan                     Adrian Bulat
           Assistant Professor                Post-Doc                              PhD student

              Computer Vision Laboratory, School of Computer Science,

                                         University of Nottingham,  UK.

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Publications

  • Muhammad Haris Khan, John McDonagh, Salman Khan, Muhammad Shahabuddin, Aditya Arora, Fahad Shahbaz Khan, Ling Shao, Georgios Tzimiropoulos, “AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces”, arXiv preprint arXiv:1909.04951, 2019 [.pdf][Dataset]
  • A. Bulat, G. Tzimiropoulos, ” Binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources “, IEEE International Conference on Computer Vision (ICCV), 2017 [.pdf]
  • A. Bulat, G. Tzimiropoulos, “How far are we from solving the 2D & 3D face alignment problem? (and a dataset of 230,000 3D facial landmarks)”, IEEE International Conference on Computer Vision (ICCV), 2017 [.pdf]
  • M.H. Khan, J. McDonagh, G. Tzimiropoulos, “Synergy between face alignment and tracking using global variable consensus optimization”, IEEE International Conference on Computer Vision (ICCV), 2017 [.pdf]
  • E. Sanchez-Lozano, G. Tzimiropoulos, B. Martinez, F. De la Torre, M. Valstar, “A Functional Regression approach to facial landmark tracking”, IEEE Transactions on Pattern Analysis & Machine Intelligence (TPAMI), 2017 [.pdf]
  • G. Tzimiropoulos, M. Pantic, “Fast algorithms for fitting Active Appearance Models to unconstrained images”, International Journal of Computer Vision (IJCV), 2017 [.pdf]
  • E. Sanchez-Lozano, B. Martinez, G. Tzimiropoulos, M. Valstar, “Cascaded continuous regression for real-time incremental face tracking”, IEEE European Conference on Computer Vision (ECCV), 2016 [.pdf]
  • A. Bulat, G. Tzimiropoulos, “Convolutional aggregation of local evidence for large pose face alignment”, British Machine Vision Conference (BMVC), 2016 [.pdf]
  • A. Bulat, G. Tzimiropoulos, “Two-stage Convolutional Part Heatmap Regression for the 1st 3D Face Alignment in the Wild (3DFAW) Challenge”, European Conference on Computer Vision Workshops (ECCV-W), 2016 [.pdf]
  • A. Jackson, M. Valstar, G. Tzimiropoulos, “A CNN cascade for landmark guided semantic part segmentation”, European Conference on Computer Vision Workshops (ECCV-W), 2016 [.pdf]
  • J. Shen, S. Zafeiriou, G. Chrysos, J. Kossaifi, G. Tzimiropoulos , M. Pantic, “The first facial landmark tracking in-the-wild challenge: Benchmark and results”, IEEE International Conference on Computer Vision Workshops (ICCV-W), 2015 [.pdf]

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