Multi-Attribute Robust Component Analysis for Facial UV Maps

The collection of large-scale three-dimensional (3-D) face models has led to significant progress in the field of 3-D face alignment “in-the-wild,” with several methods being proposed toward establishing sparse or dense 3-D correspondences between a given 2-D facial image and a 3-D face model. Utilizing 3-D face alignment improves 2-D face alignment in many ways, such as alleviating issues with artifacts and warping effects in texture images. However, the utilization of 3-D face models introduces a new set of challenges for researchers. Since facial images are commonly captured in arbitrary recording conditions, a considerable amount of missing information and gross outliers is observed (e.g., due to self-occlusion, subjects wearing eye-glasses, and so on). To this end, in this paper we propose the Multi-Attribute Robust Component Analysis (MA-RCA), a novel technique that is suitable for facial UV maps containing a considerable amount of missing information and outliers, while additionally, elegantly incorporates knowledge from various available attributes, such as age and identity. We evaluate the proposed method on problems such as UV denoising, UV completion, facial expression synthesis, and age progression, where MA-RCA outperforms compared techniques

Advances in generative modelling: from component analysis to generative adversarial networks

This Thesis revolves around datasets and algorithms, with a focus on generative modelling. In particular, we first turn our attention to a novel, multi-attribute, 2D facial dataset. We then present deterministic as well as probabilistic Component Analysis (CA) techniques which can be applied to multi-attribute 2D as well as 3D data. We finally present deep learning generative approaches specially designed to manipulate 3D facial data. Most 2D facial datasets that are available in the literature, are: a) automatically or semi-automatically collected and thus contain noisy labels, hindering the benchmarking and comparisons between algorithms. Moreover, they are not annotated for multiple attributes. In the first part of the Thesis, we present the first manually collected and annotated database, which contains labels for multiple attributes. As we demonstrate in a series of experiments, it can be used in a number of applications ranging from image translation to age-invariant face recognition. Moving on, we turn our attention to CA methodologies. CA approaches, although being able to only capture linear relationships between data, can still be proven to be efficient in data such as UV maps or 3D data registered in a common template, since they are well aligned. The introduction of more complex datasets in the literature, which contain labels for multiple attributes, naturally brought the need for novel algorithms that can simultaneously handle multiple attributes. In this Thesis, we cover novel CA approaches which are specifically designed to be utilised in datasets annotated with respect to multiple attributes and can be used in a variety of tasks, such as 2D image denoising and translation, as well as 3D data generation and identification. Nevertheless, while CA methods are indeed efficient when handling registered 3D facial data, linear 3D generative models lack details when it comes to reconstructing or generating finer facial characteristics. To alleviate this, in the final part of this Thesis we propose a novel generative framework harnessing the power of Generative Adversarial Networks.Open Acces

Neural Face Editing with Intrinsic Image Disentangling

Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and computationally intensive. In this paper, we propose an end-to-end generative adversarial network that infers a face-specific disentangled representation of intrinsic face properties, including shape (i.e. normals), albedo, and lighting, and an alpha matte. We show that this network can be trained on "in-the-wild" images by incorporating an in-network physically-based image formation module and appropriate loss functions. Our disentangling latent representation allows for semantically relevant edits, where one aspect of facial appearance can be manipulated while keeping orthogonal properties fixed, and we demonstrate its use for a number of facial editing applications.Comment: CVPR 2017 ora

FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction

We present a large-scale facial UV-texture dataset that contains over 50,000 high-quality texture UV-maps with even illuminations, neutral expressions, and cleaned facial regions, which are desired characteristics for rendering realistic 3D face models under different lighting conditions. The dataset is derived from a large-scale face image dataset namely FFHQ, with the help of our fully automatic and robust UV-texture production pipeline. Our pipeline utilizes the recent advances in StyleGAN-based facial image editing approaches to generate multi-view normalized face images from single-image inputs. An elaborated UV-texture extraction, correction, and completion procedure is then applied to produce high-quality UV-maps from the normalized face images. Compared with existing UV-texture datasets, our dataset has more diverse and higher-quality texture maps. We further train a GAN-based texture decoder as the nonlinear texture basis for parametric fitting based 3D face reconstruction. Experiments show that our method improves the reconstruction accuracy over state-of-the-art approaches, and more importantly, produces high-quality texture maps that are ready for realistic renderings. The dataset, code, and pre-trained texture decoder are publicly available at https://github.com/csbhr/FFHQ-UV.Comment: The dataset, code, and pre-trained texture decoder are publicly available at https://github.com/csbhr/FFHQ-U

Face analysis and deepfake detection

This thesis concerns deep-learning-based face-related research topics. We explore how to improve the performance of several face systems when confronting challenging variations. In Chapter 1, we provide an introduction and background information on the theme, and we list the main research questions of this dissertation. In Chapter 2, we provide a synthetic face data generator with fully controlled variations and proposed a detailed experimental comparison of main characteristics that influence face detection performance. The result shows that our synthetic dataset could complement face detectors to become more robust against specific features in the real world. Our analysis also reveals that a variety of data augmentation is necessary to address differences in performance. In Chapter 3, we propose an age estimation method for handling large pose variations for unconstrained face images. A Wasserstein-based GAN model is used to complete the full uv texture presentation. The proposed AgeGAN method simultaneously learns to capture the facial uv texture map and age characteristics.In Chapter 4, we propose a maximum mean discrepancy (MMD) based cross-domain face forgery detection. The center and triplet losses are also incorporated to ensure that the learned features are shared by multiple domains and provide better generalization abilities to unseen deep fake samples. In Chapter 5, we introduce an end-to-end framework to predict ages from face videos. Clustering based transfer learning is used to provide proper prediction for imbalanced datasets