The Menpo project

The Menpo Project [1] is a BSD-licensed set of tools and software designed to provide an end-to-end pipeline for collection and annotation of image and 3D mesh data. In particular, the Menpo Project provides tools for annotating images and meshes with a sparse set of fiducial markers that we refer to as landmarks. For example, Figure 1 shows an example of a face image that has been annotated with 68 2D landmarks. These landmarks are useful in a variety of areas in Computer Vision and Machine Learning including object detection, deformable modelling and tracking. The Menpo Project aims to enable researchers, practitioners and students to easily annotate new data sources and to investigate existing datasets. Of most interest to the Computer Vision is the fact that The Menpo Project contains completely open source implementations of a number of state-of-the-art algorithms for face detection and deformable model building. In the Menpo Project, we are actively developing and contributing to the state-of-the-art in deformable modelling [2], [3], [4], [5]. Characteristic examples of widely used state-of-the-art deformable model algorithms are Active Appearance Models [6],[7], Constrained Local Models [8], [9] and Supervised Descent Method [10]. However, there is still a noteworthy lack of high quality open source software in this area. Most existing packages are encrypted, compiled, non-maintained, partly documented, badly structured or difficult to modify. This makes them unsuitable for adoption in cutting edge scientific research. Consequently, research becomes even more difficult since performing a fair comparison between existing methods is, in most cases, infeasible. For this reason, we believe the Menpo Project represents an important contribution towards open science in the area of deformable modelling. We also believe it is important for deformable modelling to move beyond the established area of facial annotations and to extend to a wide variety of deformable object classes. We hope Menpo can accelerate this progress by providing all of our tools completely free and permissively licensed.

[1]  Simon Lucey,et al.  Deformable Model Fitting by Regularized Landmark Mean-Shift , 2010, International Journal of Computer Vision.

[2]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[3]  Simon Baker,et al.  Active Appearance Models Revisited , 2004, International Journal of Computer Vision.

[4]  Stefanos Zafeiriou,et al.  300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[5]  Maja Pantic,et al.  Gauss-Newton Deformable Part Models for Face Alignment In-the-Wild , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Stefanos Zafeiriou,et al.  Menpo: A Comprehensive Platform for Parametric Image Alignment and Visual Deformable Models , 2014, ACM Multimedia.

[7]  Maja Pantic,et al.  Active Orientation Models for Face Alignment In-the-Wild , 2014, IEEE Transactions on Information Forensics and Security.

[8]  Stefanos Zafeiriou,et al.  Robust and efficient parametric face alignment , 2011, 2011 International Conference on Computer Vision.

[9]  Georgios Tzimiropoulos,et al.  Project-Out Cascaded Regression with an application to face alignment , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Stefanos Zafeiriou,et al.  Bayesian Active Appearance Models , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Fernando De la Torre,et al.  Supervised Descent Method and Its Applications to Face Alignment , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Josephine Sullivan,et al.  One millisecond face alignment with an ensemble of regression trees , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Stefanos Zafeiriou,et al.  Unifying holistic and Parts-Based Deformable Model fitting , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Stefanos Zafeiriou,et al.  Active Pictorial Structures , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Stefanos Zafeiriou,et al.  Incremental Face Alignment in the Wild , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Stefanos Zafeiriou,et al.  From Pixels to Response Maps: Discriminative Image Filtering for Face Alignment in the Wild , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Stefanos Zafeiriou,et al.  Feature-Based Lucas–Kanade and Active Appearance Models , 2015, IEEE Transactions on Image Processing.

[18]  Petros Maragos,et al.  Adaptive and constrained algorithms for inverse compositional Active Appearance Model fitting , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Matthew Turk,et al.  A Morphable Model For The Synthesis Of 3D Faces , 1999, SIGGRAPH.