Analysis of 3D Face Reconstruction

This thesis investigates the long standing problem of 3D reconstruction from a single 2D face image. Face reconstruction from a single 2D face image is an ill posed problem involving estimation of the intrinsic and the extrinsic camera parameters, light parameters, shape parameters and the texture parameters. The proposed approach has many potential applications in the law enforcement, surveillance, medicine, computer games and the entertainment industries. This problem is addressed using an analysis by synthesis framework by reconstructing a 3D face model from identity photographs. The identity photographs are a widely used medium for face identi cation and can be found on identity cards and passports. The novel contribution of this thesis is a new technique for creating 3D face models from a single 2D face image. The proposed method uses the improved dense 3D correspondence obtained using rigid and non-rigid registration techniques. The existing reconstruction methods use the optical ow method for establishing 3D correspondence. The resulting 3D face database is used to create a statistical shape model. The existing reconstruction algorithms recover shape by optimizing over all the parameters simultaneously. The proposed algorithm simplifies the reconstruction problem by using a step wise approach thus reducing the dimension of the parameter space and simplifying the opti- mization problem. In the alignment step, a generic 3D face is aligned with the given 2D face image by using anatomical landmarks. The texture is then warped onto the 3D model by using the spatial alignment obtained previously. The 3D shape is then recovered by optimizing over the shape parameters while matching a texture mapped model to the target image. There are a number of advantages of this approach. Firstly, it simpli es the optimization requirements and makes the optimization more robust. Second, there is no need to accurately recover the illumination parameters. Thirdly, there is no need for recovering the texture parameters by using a texture synthesis approach. Fourthly, quantitative analysis is used for improving the quality of reconstruction by improving the cost function. Previous methods use qualitative methods such as visual analysis, and face recognition rates for evaluating reconstruction accuracy. The improvement in the performance of the cost function occurs as a result of improvement in the feature space comprising the landmark and intensity features. Previously, the feature space has not been evaluated with respect to reconstruction accuracy thus leading to inaccurate assumptions about its behaviour. The proposed approach simpli es the reconstruction problem by using only identity images, rather than placing eff ort on overcoming the pose, illumination and expression (PIE) variations. This makes sense, as frontal face images under standard illumination conditions are widely available and could be utilized for accurate reconstruction. The reconstructed 3D models with texture can then be used for overcoming the PIE variations

A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization

We propose NeuFace, a 3D face mesh pseudo annotation method on videos via neural re-parameterized optimization. Despite the huge progress in 3D face reconstruction methods, generating reliable 3D face labels for in-the-wild dynamic videos remains challenging. Using NeuFace optimization, we annotate the per-view/-frame accurate and consistent face meshes on large-scale face videos, called the NeuFace-dataset. We investigate how neural re-parameterization helps to reconstruct image-aligned facial details on 3D meshes via gradient analysis. By exploiting the naturalness and diversity of 3D faces in our dataset, we demonstrate the usefulness of our dataset for 3D face-related tasks: improving the reconstruction accuracy of an existing 3D face reconstruction model and learning 3D facial motion prior. Code and datasets will be available at https://neuface-dataset.github.io.Comment: 9 pages, 7 figures, and 3 tables for the main paper. 8 pages, 6 figures and 3 tables for the appendi

3D Face Reconstruction: the Road to Forensics

3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bringing evidence to a lawsuit unclear. An extensive investigation of the constraints, potential, and limits of its application in forensics is still missing. Shedding some light on this matter is the goal of the present survey, which starts by clarifying the relation between forensic applications and biometrics, with a focus on face recognition. Therefore, it provides an analysis of the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discusses the current obstacles that separate 3D face reconstruction from an active role in forensic applications. Finally, it examines the underlying data sets, with their advantages and limitations, while proposing alternatives that could substitute or complement them

Adaptive face modelling for reconstructing 3D face shapes from single 2D images

Example-based statistical face models using principle component analysis (PCA) have been widely deployed for three-dimensional (3D) face reconstruction and face recognition. The two common factors that are generally concerned with such models are the size of the training dataset and the selection of different examples in the training set. The representational power (RP) of an example-based model is its capability to depict a new 3D face for a given 2D face image. The RP of the model can be increased by correspondingly increasing the number of training samples. In this contribution, a novel approach is proposed to increase the RP of the 3D face reconstruction model by deforming a set of examples in the training dataset. A PCA-based 3D face model is adapted for each new near frontal input face image to reconstruct the 3D face shape. Further an extended Tikhonov regularisation method has been

3D Face Reconstruction: the Road to Forensics

3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bringing evidence to a lawsuit unclear. An extensive investigation of the constraints, potential, and limits of its application in forensics is still missing. Shedding some light on this matter is the goal of the present survey, which starts by clarifying the relation between forensic applications and biometrics, with a focus on face recognition. Therefore, it provides an analysis of the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discusses the current obstacles that separate 3D face reconstruction from an active role in forensic applications. Finally, it examines the underlying data sets, with their advantages and limitations, while proposing alternatives that could substitute or complement them.Comment: The manuscript has been accepted for publication in ACM Computing Surveys. arXiv admin note: text overlap with arXiv:2303.1116

Reconstruction of three-dimensional facial geometric features related to fetal alcohol syndrome using adult surrogates

Fetal alcohol syndrome (FAS) is a condition caused by prenatal alcohol exposure. The diagnosis of FAS is based on the presence of central nervous system impairments, evidence of growth abnormalities and abnormal facial features. Direct anthropometry has traditionally been used to obtain facial data to assess the FAS facial features. Research efforts have focused on indirect anthropometry such as 3D surface imaging systems to collect facial data for facial analysis. However, 3D surface imaging systems are costly. As an alternative, approaches for 3D reconstruction from a single 2D image of the face using a 3D morphable model (3DMM) were explored in this research study. The research project was accomplished in several steps. 3D facial data were obtained from the publicly available BU-3DFE database, developed by the State University of New York. The 3D face scans in the training set were landmarked by different observers. The reliability and precision in selecting 3D landmarks were evaluated. The intraclass correlation coefficients for intra- and inter-observer reliability were greater than 0.95. The average intra-observer error was 0.26 mm and the average inter-observer error was 0.89 mm. A rigid registration was performed on the 3D face scans in the training set. Following rigid registration, a dense point-to-point correspondence across a set of aligned face scans was computed using the Gaussian process model fitting approach. A 3DMM of the face was constructed from the fully registered 3D face scans. The constructed 3DMM of the face was evaluated based on generalization, specificity, and compactness. The quantitative evaluations show that the constructed 3DMM achieves reliable results. 3D face reconstructions from single 2D images were estimated based on the 3DMM. The MetropolisHastings algorithm was used to fit the 3DMM features to 2D image features to generate the 3D face reconstruction. Finally, the geometric accuracy of the reconstructed 3D faces was evaluated based on ground-truth 3D face scans. The average root mean square error for the surface-to-surface comparisons between the reconstructed faces and the ground-truth face scans was 2.99 mm. In conclusion, a framework to estimate 3D face reconstructions from single 2D facial images was developed and the reconstruction errors were evaluated. The geometric accuracy of the 3D face reconstructions was comparable to that found in the literature. However, future work should consider minimizing reconstruction errors to acceptable clinical standards in order for the framework to be useful for 3D-from-2D reconstruction in general, and also for developing FAS applications. Finally, future work should consider estimating a 3D face using multi-view 2D images to increase the information available for 3D-from-2D reconstruction

CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images

With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data. With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence, 201

Face Recognition based on CNN 2D-3D Reconstruction using Shape and Texture Vectors Combining

This study proposes a face recognition model using a combination of shape and texture vectors that are used to produce new face images on 2D-3D reconstruction images. The reconstruction process to produce 3D face images is carried out using the convolutional neural network (CNN) method on 2D face images. Merging shapes and textures vector is used to produce correlation points on new face images that have similarities to the initial image used. Principal Component Analysis (PCA) is used as a feature extraction method, for the classification method we use the Mahalanobis method. The results of the tests can produce a better recognition rate compared to face recognition testing using 2D images