This paper proposes an approach to learn generic multi-modal mesh surface representations using a novel scheme for fusing texture and geometric data. Our approach defines an inverse mapping between different geometric descriptors computed on the mesh surface or its down-sampled version, and the corresponding 2D texture image of the mesh, allowing the construction of fused geometrically augmented images (FGAI). This new fused modality enables us to learn feature representations from 3D data in a highly efficient manner by simply employing standard convolutional neural networks in a transfer-learning mode. In contrast to existing methods, the proposed approach is both computationally and memory efficient, preserves intrinsic geometric information and learns highly discriminative feature representation by effectively fusing shape and texture information at data level. The efficacy of our approach is demonstrated for the tasks of facial action unit detection and expression classification. The extensive experiments conducted on the Bosphorus and BU-4DFE datasets, show that our method produces a significant boost in the performance when compared to state-of-the-art solutions
Model-based approaches first establish a dense point-to-point correspondence between a query mesh and a generic expression deformable mesh using rigid and non-rigid transformation techniques. The transformation parameters are then used as representations of the query mesh for classification. Non-rigid facial deformations are characterized by a bilinear deformable model in @cite_14 . The shape of a scan with facial expression is decomposed into neutral and expression parts in @cite_22 . The expression part of the decomposed scan is then employed for encoding the facial scan. Some works combine the strengths of both feature-based and model-based techniques. For example, @cite_15 first segment the face into multiple regions based upon muscular movements. Geometric descriptors are then extracted from these regions, followed by a fusion scheme weights to optimally combine decisions from different regions.
@cite_15 @cite_14 @cite_22
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Effectively characterizing the behavior of deformable objects has wide applicability but remains challenging. We present a new rotation-invariant deformation representation and a novel reconstruction algorithm to accurately reconstruct the positions and local rotations simultaneously. Meshes can be very efficiently reconstructed from our representation by matrix pre-decomposition, while, at the same time, hard or soft constraints can be flexibly specified with only positions of handles needed. Our approach is thus particularly suitable for constrained deformations guided by examples, providing significant benefits over state-of-the-art methods. Based on this, we further propose novel data-driven approaches to mesh deformation and non-rigid registration of deformable objects. Both problems are formulated consistently as finding an optimized model in the shape space that satisfies boundary constraints, either specified by the user, or according to the scan. By effectively exploiting the knowledge in the shape space, our method produces realistic deformation results in real-time and produces high quality registrations from a template model to a single noisy scan captured using a low-quality depth camera, outperforming state-of-the-art methods. We present a multi-view stereo reconstruction technique that directly produces a complete high-fidelity head model with consistent facial mesh topology. While existing techniques decouple shape estimation and facial tracking, our framework jointly optimizes for stereo constraints and consistent mesh parameterization. Our method is therefore free from drift and fully parallelizable for dynamic facial performance capture. We produce highly detailed facial geometries with artist-quality UV parameterization, including secondary elements such as eyeballs, mouth pockets, nostrils, and the back of the head. Our approach consists of deforming a common template model to match multi-view input images of the subject, while satisfying cross-view, cross-subject, and cross-pose consistencies using a combination of 2D landmark detection, optical flow, and surface and volumetric Laplacian regularization. Since the flow is never computed between frames, our method is trivially parallelized by processing each frame independently. Accurate rigid head pose is extracted using a PCA-based dimension reduction and denoising scheme. We demonstrate high-fidelity performance capture results with challenging head motion and complex facial expressions around eye and mouth regions. While the quality of our results is on par with the current state-of-the-art, our approach can be fully parallelized, does not suffer from drift, and produces face models with production-quality mesh topologies. In this work, we propose and experiment an original solution to 3D face recognition that supports face matching also in the case of probe scans with missing parts. In the proposed approach, distinguishing traits of the face are captured by first extracting 3D keypoints of the scan and then measuring how the face surface changes in the keypoints neighborhood using local shape descriptors. In particular: 3D keypoints detection relies on the adaptation to the case of 3D faces of the meshDOG algorithm that has been demonstrated to be effective for 3D keypoints extraction from generic objects; as 3D local descriptors we used the HOG descriptor and also proposed two alternative solutions that develop, respectively, on the histogram of orientations and the geometric histogram descriptors. Face similarity is evaluated by comparing local shape descriptors across inlier pairs of matching keypoints between probe and gallery scans. The face recognition accuracy of the approach has been first experimented on the difficult probes included in the new 2D 3D Florence face dataset that has been recently collected and released at the University of Firenze, and on the Binghamton University 3D facial expression dataset. Then, a comprehensive comparative evaluation has been performed on the Bosphorus, Gavab and UND FRGC v2.0 databases, where competitive results with respect to existing solutions for 3D face biometrics have been obtained. Graphical abstractDisplay Omitted Highlights3D face recognition approach deployable in real non-cooperative contexts of use.Fully-3D approach, based on keypoints detection, description and matching.MeshDOG keypoints detector combined with the multi-ring GH descriptor.RANSAC algorithm included for outlier removal from matching keypoints.State of the art accuracy for recognizing 3D scans with missing parts. We propose a feature-based 2D+3D multimodal facial expression recognition method.It is fully automatic benefit from a large set of automatically detected landmarks.The complementarities between 2D and 3D features are comprehensively demonstrated.Our method achieves the best accuracy on the BU-3DFE database so far.A good generalization ability is shown on the Bosphorus database. We present a fully automatic multimodal 2D + 3D feature-based facial expression recognition approach and demonstrate its performance on the BU-3DFE database. Our approach combines multi-order gradient-based local texture and shape descriptors in order to achieve efficiency and robustness. First, a large set of fiducial facial landmarks of 2D face images along with their 3D face scans are localized using a novel algorithm namely incremental Parallel Cascade of Linear Regression (iPar-CLR). Then, a novel Histogram of Second Order Gradients (HSOG) based local image descriptor in conjunction with the widely used first-order gradient based SIFT descriptor are used to describe the local texture around each 2D landmark. Similarly, the local geometry around each 3D landmark is described by two novel local shape descriptors constructed using the first-order and the second-order surface differential geometry quantities, i.e., Histogram of mesh Gradients (meshHOG) and Histogram of mesh Shape index (curvature quantization, meshHOS). Finally, the Support Vector Machine (SVM) based recognition results of all 2D and 3D descriptors are fused at both feature-level and score-level to further improve the accuracy. Comprehensive experimental results demonstrate that there exist impressive complementary characteristics between the 2D and 3D descriptors. We use the BU-3DFE benchmark to compare our approach to the state-of-the-art ones. Our multimodal feature-based approach outperforms the others by achieving an average recognition accuracy of 86.32 . Moreover, a good generalization ability is shown on the Bosphorus database.