AgeDB: the first manually collected, in-the-wild age database

Over the last few years, increased interest has arisen with respect to age-related tasks in the Computer Vision community. As a result, several "in-the-wild" databases annotated with respect to the age attribute became available in the literature. Nevertheless, one major drawback of these databases is that they are semi-automatically collected and annotated and thus they contain noisy labels. Therefore, the algorithms that are evaluated in such databases are prone to noisy estimates. In order to overcome such drawbacks, we present in this paper the first, to the best of knowledge, manually collected "in-the-wild" age database, dubbed AgeDB, containing images annotated with accurate to the year, noise-free labels. As demonstrated by a series of experiments utilizing state-of-the-art algorithms, this unique property renders AgeDB suitable when performing experiments on age-invariant face verification, age estimation and face age progression "in-the-wild"

Initializing probabilistic linear discriminant analysis

Component Analysis (CA) consists of a set of statistical techniques that decompose data to appropriate latent components that are relevant to the task-at-hand (e.g., clustering, segmentation, classification, alignment). During the past few years, an explosion of research in probabilistic CA has been witnessed, with the introduction of several novel methods (e.g., Probabilistic Principal Component Analysis, Probabilistic Linear Discriminant Analysis (PLDA), Probabilistic Canonical Correlation Analysis). PLDA constitutes one of the most widely used supervised CA techniques which is utilized in order to extract suitable, distinct subspaces by exploiting the knowledge of data annotated in terms of different labels. Nevertheless, an inherent difficulty in PLDA variants is the proper initialization of the parameters in order to avoid ending up in poor local maxima. In this light, we propose a novel method to initialize the parameters in PLDA in a consistent and robust way. The performance of the algorithm is demonstrated via a set of experiments on the modified XM2VTS database, which is provided by the authors of the original PLDA model

AvatarMe: realistically renderable 3D facial reconstruction "in-the-wild"

Over the last years, with the advent of Generative Adversarial Networks (GANs), many face analysis tasks have accomplished astounding performance, with applications including, but not limited to, face generation and 3D face reconstruction from a single "in-the-wild" image. Nevertheless, to the best of our knowledge, there is no method which can produce high-resolution photorealistic 3D faces from "in-the-wild" images and this can be attributed to the: (a) scarcity of available data for training, and (b) lack of robust methodologies that can successfully be applied on very high-resolution data. In this paper, we introduce AvatarMe, the first method that is able to reconstruct photorealistic 3D faces from a single "in-the-wild" image with an increasing level of detail. To achieve this, we capture a large dataset of facial shape and reflectance and build on a state-of-the-art 3D texture and shape reconstruction method and successively refine its results, while generating the per-pixel diffuse and specular components that are required for realistic rendering. As we demonstrate in a series of qualitative and quantitative experiments, AvatarMe outperforms the existing arts by a significant margin and reconstructs authentic, 4K by 6K-resolution 3D faces from a single low-resolution image that, for the first time, bridges the uncanny valley