21,243 research outputs found
Learning based automatic face annotation for arbitrary poses and expressions from frontal images only
Statistical approaches for building non-rigid deformable models, such as the active appearance model (AAM), have enjoyed great popularity in recent years, but typically require tedious manual annotation of training images. In this paper, a learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking. This approach employs the idea of initially learning the correspondences between landmarks in a frontal image and a set of training images with a face in arbitrary poses. Using this learner, virtual images of unseen faces at any arbitrary pose for which the learner was trained can be reconstructed by predicting the new landmark locations and warping the texture from the frontal image. View-based AAMs are then built from the virtual images and used for automatically annotating unseen images, including images of different facial expressions, at any random pose within the maximum range spanned by the virtually reconstructed images. The approach is experimentally validated by automatically annotating face images from three different databases
Facial Point Detection using Boosted Regression and Graph Models
Finding fiducial facial points in any frame of a video showing rich naturalistic facial behaviour is an unsolved problem. Yet this is a crucial step for geometric-featurebased facial expression analysis, and methods that use appearance-based features extracted at fiducial facial point locations. In this paper we present a method based on a combination of Support Vector Regression and Markov Random Fields to drastically reduce the time needed to search for a pointās location and increase the accuracy and robustness of the algorithm. Using Markov Random Fields allows us to constrain the search space by exploiting the constellations that facial points can form. The regressors on the other hand learn a mapping between the appearance of the area surrounding a point and the positions of these points, which makes detection of the points very fast and can make the algorithm robust to variations of appearance due to facial expression and moderate changes in head pose. The proposed point detection algorithm was tested on 1855 images, the results of which showed we outperform current state of the art point detectors
An update on statistical boosting in biomedicine
Statistical boosting algorithms have triggered a lot of research during the
last decade. They combine a powerful machine-learning approach with classical
statistical modelling, offering various practical advantages like automated
variable selection and implicit regularization of effect estimates. They are
extremely flexible, as the underlying base-learners (regression functions
defining the type of effect for the explanatory variables) can be combined with
any kind of loss function (target function to be optimized, defining the type
of regression setting). In this review article, we highlight the most recent
methodological developments on statistical boosting regarding variable
selection, functional regression and advanced time-to-event modelling.
Additionally, we provide a short overview on relevant applications of
statistical boosting in biomedicine
Pose-Invariant 3D Face Alignment
Face alignment aims to estimate the locations of a set of landmarks for a
given image. This problem has received much attention as evidenced by the
recent advancement in both the methodology and performance. However, most of
the existing works neither explicitly handle face images with arbitrary poses,
nor perform large-scale experiments on non-frontal and profile face images. In
order to address these limitations, this paper proposes a novel face alignment
algorithm that estimates both 2D and 3D landmarks and their 2D visibilities for
a face image with an arbitrary pose. By integrating a 3D deformable model, a
cascaded coupled-regressor approach is designed to estimate both the camera
projection matrix and the 3D landmarks. Furthermore, the 3D model also allows
us to automatically estimate the 2D landmark visibilities via surface normals.
We gather a substantially larger collection of all-pose face images to evaluate
our algorithm and demonstrate superior performances than the state-of-the-art
methods
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