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Menpo: A Comprehensive Platform for Parametric Image Alignment and Visual Deformable Models

Published:03 November 2014Publication History

ABSTRACT

The Menpo Project, hosted at http://www.menpo.io, is a BSD-licensed software platform providing a complete and comprehensive solution for annotating, building, fitting and evaluating deformable visual models from image data. Menpo is a powerful and flexible cross-platform framework written in Python that works on Linux, OS X and Windows. Menpo has been designed to allow for easy adaptation of Lucas-Kanade (LK) parametric image alignment techniques, and goes a step further in providing all the necessary tools for building and fitting state-of-the-art deformable models such as Active Appearance Models (AAMs), Constrained Local Models (CLMs) and regression-based methods (such as the Supervised Descent Method (SDM)). These methods are extensively used for facial point localisation although they can be applied to many other deformable objects. Menpo makes it easy to understand and evaluate these complex algorithms, providing tools for visualisation, analysis, and performance assessment. A key challenge in building deformable models is data annotation; Menpo expedites this process by providing a simple web-based annotation tool hosted at http://www.landmarker.io. The Menpo Project is thoroughly documented and provides extensive examples for all of its features. We believe the project is ideal for researchers, practitioners and students alike.

References

  1. S. Baker and I. Matthews, Lucas-kanade 20 years on: A unifying framework," IJCV, vol. 56, no. 3, pp. 221--255, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. T. Cootes et al., Active appearance models," IEEE T-PAMI, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. E. Antonakos et al., Hog active appearance models," in ICIP, 2014.Google ScholarGoogle Scholar
  4. J. Saragih et al., Deformable model fitting by regularized landmark mean-shift," IJCV, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. X. Xiong and F. De la Torre, Supervised descent method and its applications to face alignment," in CVPR, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. J. Saragih and R. Goecke,Learning aam fitting through simulation," Pat. Rec., 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. G. Tzimiropoulos et al., Robust and efficient parametric face alignment," in ICCV, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. G. Tzimiropoulos et al., Generic active appearance models revisited," in ACCV, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. G. Papandreou and P. Maragos, Adaptive and constrained algorithms for inverse compositional active appearance model fitting," in CVPR, 2008.Google ScholarGoogle Scholar
  10. T. Ojala et al., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE T-PAMI, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. N. Dalal and B. Triggs, Histograms of oriented gradients for human detection," in CVPR, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. A. Asthana et al., Robust discriminative response map fitting with constrained local models," in CVPR, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. F. P--erez and B. Granger, IPython: a system for interactive scientific computing," Computing in Science and Engineering, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. P. N. Belhumeur et al., Localizing parts of faces using a consensus of exemplars," in CVPR, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. C. Sagonas et al., 300 faces in-the-wild challenge: the first facial landmark localization challenge," in ICCV-W 2013, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. X. Zhu and D. Ramanan, Face detection, pose estimation, and landmark localization in the wild," in CVPR, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Conferences
        MM '14: Proceedings of the 22nd ACM international conference on Multimedia
        November 2014
        1310 pages
        ISBN:9781450330633
        DOI:10.1145/2647868

        Copyright © 2014 ACM

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        Publication History

        • Published: 3 November 2014

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