Automatic landmark annotation and dense correspondence registration for 3D human facial images

Dense surface registration of three-dimensional (3D) human facial images holds great potential for studies of human trait diversity, disease genetics, and forensics. Non-rigid registration is particularly useful for establishing dense anatomical correspondences between faces. Here we describe a novel non-rigid registration method for fully automatic 3D facial image mapping. This method comprises two steps: first, seventeen facial landmarks are automatically annotated, mainly via PCA-based feature recognition following 3D-to-2D data transformation. Second, an efficient thin-plate spline (TPS) protocol is used to establish the dense anatomical correspondence between facial images, under the guidance of the predefined landmarks. We demonstrate that this method is robust and highly accurate, even for different ethnicities. The average face is calculated for individuals of Han Chinese and Uyghur origins. While fully automatic and computationally efficient, this method enables high-throughput analysis of human facial feature variation.Comment: 33 pages, 6 figures, 1 tabl

Easy Rigging of Face by Automatic Registration and Transfer of Skinning Parameters

International audiencePreparing a facial mesh to be animated requires a laborious manualrigging process. The rig specifies how the input animation datadeforms the surface and allows artists to manipulate a character.We present a method that automatically rigs a facial mesh based onRadial Basis Functions and linear blend skinning approach.Our approach transfers the skinning parameters (feature points andtheir envelopes, ie. point-vertex weights),of a reference facial mesh (source) - already rigged - tothe chosen facial mesh (target) by computing an automaticregistration between the two meshes.There is no need to manually mark the correspondence between thesource and target mesh.As a result, inexperienced artists can automatically rig facial meshes and startright away animating their 3D characters, driven for instanceby motion capture data

Active nonrigid ICP algorithm

© 2015 IEEE.The problem of fitting a 3D facial model to a 3D mesh has received a lot of attention the past 15-20 years. The majority of the techniques fit a general model consisting of a simple parameterisable surface or a mean 3D facial shape. The drawback of this approach is that is rather difficult to describe the non-rigid aspect of the face using just a single facial model. One way to capture the 3D facial deformations is by means of a statistical 3D model of the face or its parts. This is particularly evident when we want to capture the deformations of the mouth region. Even though statistical models of face are generally applied for modelling facial intensity, there are few approaches that fit a statistical model of 3D faces. In this paper, in order to capture and describe the non-rigid nature of facial surfaces we build a part-based statistical model of the 3D facial surface and we combine it with non-rigid iterative closest point algorithms. We show that the proposed algorithm largely outperforms state-of-the-art algorithms for 3D face fitting and alignment especially when it comes to the description of the mouth region

Algorithms For Automatic And Robust Registration Of 3D Head Scans

wo methods for registering laser-scans of human heads and transforming them to a new semantically consistent topology defined by a user-provided template mesh are described. Both algorithms are stated within the Iterative Closest Point framework. The first method is based on finding landmark correspondences by iteratively registering the vicinity of a landmark with a re-weighted error function. Thin-plate spline interpolation is then used to deform the template mesh and finally the scan is resampled in the topology of the deformed template. The second algorithm employs a morphable shape model, which can be computed from a database of laser-scans using the first algorithm. It directly optimizes pose and shape of the morphable model. The use of the algorithm with PCA mixture models, where the shape is split up into regions each described by an individual subspace, is addressed. Mixture models require either blending or regularization strategies, both of which are described in detail. For both algorithms, strategies for filling in missing geometry for incomplete laser-scans are described. While an interpolation-based approach can be used to fill in small or smooth regions, the model-driven algorithm is capable of fitting a plausible complete head mesh to arbitrarily small geometry, which is known as "shape completion". The importance of regularization in the case of extreme shape completion is shown