Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking

In this paper, we propose a generative framework that unifies depth-based 3D facial pose tracking and face model adaptation on-the-fly, in the unconstrained scenarios with heavy occlusions and arbitrary facial expression variations. Specifically, we introduce a statistical 3D morphable model that flexibly describes the distribution of points on the surface of the face model, with an efficient switchable online adaptation that gradually captures the identity of the tracked subject and rapidly constructs a suitable face model when the subject changes. Moreover, unlike prior art that employed ICP-based facial pose estimation, to improve robustness to occlusions, we propose a ray visibility constraint that regularizes the pose based on the face model's visibility with respect to the input point cloud. Ablation studies and experimental results on Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective and outperforms completing state-of-the-art depth-based methods

Dense 3D Face Correspondence

We present an algorithm that automatically establishes dense correspondences between a large number of 3D faces. Starting from automatically detected sparse correspondences on the outer boundary of 3D faces, the algorithm triangulates existing correspondences and expands them iteratively by matching points of distinctive surface curvature along the triangle edges. After exhausting keypoint matches, further correspondences are established by generating evenly distributed points within triangles by evolving level set geodesic curves from the centroids of large triangles. A deformable model (K3DM) is constructed from the dense corresponded faces and an algorithm is proposed for morphing the K3DM to fit unseen faces. This algorithm iterates between rigid alignment of an unseen face followed by regularized morphing of the deformable model. We have extensively evaluated the proposed algorithms on synthetic data and real 3D faces from the FRGCv2, Bosphorus, BU3DFE and UND Ear databases using quantitative and qualitative benchmarks. Our algorithm achieved dense correspondences with a mean localisation error of 1.28mm on synthetic faces and detected $14$ anthropometric landmarks on unseen real faces from the FRGCv2 database with 3mm precision. Furthermore, our deformable model fitting algorithm achieved 98.5% face recognition accuracy on the FRGCv2 and 98.6% on Bosphorus database. Our dense model is also able to generalize to unseen datasets.Comment: 24 Pages, 12 Figures, 6 Tables and 3 Algorithm

Spatially-dense 3D facial asymmetry assessment in both typical and disordered growth

Mild facial asymmetries are common in typical growth patterns. Therefore, detection of disordered facial growth patterns in individuals characterized by asymmetries is preferably accomplished by reference to the typical variation found in the general population rather than to some ideal of perfect symmetry, which rarely exists. This presents a challenge in developing an asymmetry assessment tool that is applicable, without modification, to detect both mild and severe facial asymmetries. In this paper we use concepts from geometric morphometrics to obtain robust and spatially-dense asymmetry assessments using a superimposition protocol for comparison of a face with its mirror image. Spatially-dense localization of asymmetries was achieved using an anthropometric mask consisting of uniformly sampled quasi-landmarks that were automatically indicated on 3D facial images. Robustness, in the sense of an unbiased analysis under increasing asymmetry, was ensured by an adaptive, robust, least-squares superimposition. The degree of overall asymmetry in an individual was scored using a root-mean-squared-error, and the proportion was scored using a novel relative significant asymmetry percentage. This protocol was applied to a database of 3D facial images from 359 young healthy individuals and three individuals with disordered facial growth. Typical asymmetry statistics were derived and were mainly located on, but not limited to, the lower two-thirds of the face in males and females. The asymmetry in males was more extensive and of a greater magnitude than in females. This protocol and proposed scoring of asymmetry with accompanying reference statistics will be useful for the detection and quantification of facial asymmetry in future studies

3D facial landmark localization using combinatorial search and shape regression

This paper presents a method for the automatic detection of facial landmarks. The algorithm receives a set of 3D candidate points for each landmark (e.g. from a feature detector) and performs combinatorial search constrained by a deformable shape model. A key assumption of our approach is that for some landmarks there might not be an accurate candidate in the input set. This is tackled by detecting partial subsets of landmarks and inferring those that are missing so that the probability of the deformable model is maximized. The ability of the model to work with incomplete information makes it possible to limit the number of candidates that need to be retained, substantially reducing the number of possible combinations to be tested with respect to the alternative of trying to always detect the complete set of landmarks. We demonstrate the accuracy of the proposed method in a set of 144 facial scans acquired by means of a hand-held laser scanner in the context of clinical craniofacial dysmorphology research. Using spin images to describe the geometry and targeting 11 facial landmarks, we obtain an average error below 3 mm, which compares favorably with other state of the art approaches based on geometric descriptors

Robust signatures for 3D face registration and recognition

PhDBiometric authentication through face recognition has been an active area of research for the last few decades, motivated by its application-driven demand. The popularity of face recognition, compared to other biometric methods, is largely due to its minimum requirement of subject co-operation, relative ease of data capture and similarity to the natural way humans distinguish each other. 3D face recognition has recently received particular interest since three-dimensional face scans eliminate or reduce important limitations of 2D face images, such as illumination changes and pose variations. In fact, three-dimensional face scans are usually captured by scanners through the use of a constant structured-light source, making them invariant to environmental changes in illumination. Moreover, a single 3D scan also captures the entire face structure and allows for accurate pose normalisation. However, one of the biggest challenges that still remain in three-dimensional face scans is the sensitivity to large local deformations due to, for example, facial expressions. Due to the nature of the data, deformations bring about large changes in the 3D geometry of the scan. In addition to this, 3D scans are also characterised by noise and artefacts such as spikes and holes, which are uncommon with 2D images and requires a pre-processing stage that is speci c to the scanner used to capture the data. The aim of this thesis is to devise a face signature that is compact in size and overcomes the above mentioned limitations. We investigate the use of facial regions and landmarks towards a robust and compact face signature, and we study, implement and validate a region-based and a landmark-based face signature. Combinations of regions and landmarks are evaluated for their robustness to pose and expressions, while the matching scheme is evaluated for its robustness to noise and data artefacts

Fast and Accurate 3D Face Recognition Using Registration to an Intrinsic Coordinate System and Fusion of Multiple Region classifiers

In this paper we present a new robust approach for 3D face registration to an intrinsic coordinate system of the face. The intrinsic coordinate system is defined by the vertical symmetry plane through the nose, the tip of the nose and the slope of the bridge of the nose. In addition, we propose a 3D face classifier based on the fusion of many dependent region classifiers for overlapping face regions. The region classifiers use PCA-LDA for feature extraction and the likelihood ratio as a matching score. Fusion is realised using straightforward majority voting for the identification scenario. For verification, a voting approach is used as well and the decision is defined by comparing the number of votes to a threshold. Using the proposed registration method combined with a classifier consisting of 60 fused region classifiers we obtain a 99.0% identification rate on the all vs first identification test of the FRGC v2 data. A verification rate of 94.6% at FAR=0.1% was obtained for the all vs all verification test on the FRGC v2 data using fusion of 120 region classifiers. The first is the highest reported performance and the second is in the top-5 of best performing systems on these tests. In addition, our approach is much faster than other methods, taking only 2.5 seconds per image for registration and less than 0.1 ms per comparison. Because we apply feature extraction using PCA and LDA, the resulting template size is also very small: 6 kB for 60 region classifiers

Setting a world record in 3D face recognition

Biometrics - recognition of persons based on how they look or behave, is the main subject of research at the Chair of Biometric Pattern Recognition (BPR) of the Services, Cyber Security and Safety Group (SCS) of the EEMCS Faculty at the University of Twente. Examples are finger print recognition, iris and face recognition. A relatively new field is 3D face recognition based on the shape of the face rather that its appearance. This paper presents a method for 3D face recognition developed at the Chair of Biometric Pattern Recognition (BPR) of the Services, Cyber Security and Safety Group (SCS) of the EEMCS Faculty at the University of Twente and published in 2011. The paper also shows that noteworthy performance gains can be obtained by optimisation of an existing method. The method is based on registration to an intrinsic coordinate system using the vertical symmetry plane of the head, the tip of the nose and the slope of the nose bridge. For feature extraction and classification multiple regional PCA-LDA-likelihood ratio based classifiers are fused using a fixed FAR voting strategy. We present solutions for correction of motion artifacts in 3D scans, improved registration and improved training of the used PCA-LDA classifier using automatic outlier removal. These result in a notable improvement of the recognition rates. The all vs all verification rate for the FRGC v2 dataset jumps to 99.3% and the identification rate for the all vs first to 99.4%. Both are to our knowledge the best results ever obtained for these benchmarks by a fairly large margin

Non-Rigid Registration with Deep Learning and Conformal Harmonic Maps

We present a novel fully-automated approach to non-rigid registration for high-resolution facial scans using conformal harmonic maps. The novelty of this paper is its use of applied deep learning models to prepare data for geometric algorithms to compute non-rigid registration. We use facial detection to both constrain the boundary of the face and provide a mechanism to manipulate the input mesh. We use conformal harmonic maps[7] to map a dense 3D point cloud to the closed unit disc D1(0) and optimize the weights of each edge. Our experiments show the effectiveness of this approach