Facial aging and facial rejuvenation analyze a given face photograph to predict a future look or estimate a past look of the person. To achieve this, it is critical to preserve human identity and the corresponding aging progression and regression with high accuracy. However, existing methods cannot simultaneously handle these two objectives well. We propose a novel generative adversarial network based approach, named the Conditional Multi-Adversarial AutoEncoder with Ordinal Regression (CMAAE-OR). It utilizes an age estimation technique to control the aging accuracy and takes a high-level feature representation to preserve personalized identity. Specifically, the face is first mapped to a latent vector through a convolutional encoder. The latent vector is then projected onto the face manifold conditional on the age through a deconvolutional generator. The latent vector preserves personalized face features and the age controls facial aging and rejuvenation. A discriminator and an ordinal regression are imposed on the encoder and the generator in tandem, making the generated face images to be more photorealistic while simultaneously exhibiting desirable aging effects. Besides, a high-level feature representation is utilized to preserve personalized identity of the generated face. Experiments on two benchmark datasets demonstrate appealing performance of the proposed method over the state-of-the-art.
Early approaches were mainly based on the skin's anatomical structure and simulated the profile growth and facial muscle changes with respect to the elapsed time @cite_22 . Although these methods provided novel insights for facial aging synthesis, they are difficult to generalize for other tasks because of their complex modeling techniques.
@cite_22
2541527415 1516424378 2950973664 2140190219
We propose a two fold approach towards modeling facial aging in adults. Firstly, we develop a shape transformation model that is formulated as a physically-based parametric muscle model that captures the subtle deformations facial features undergo with age. The model implicitly accounts for the physical properties and geometric orientations of the individual facial muscles. Next, we develop an image gradient based texture transformation function that characterizes facial wrinkles and other skin artifacts often observed during different ages. Facial growth statistics (both in terms of shape and texture) play a crucial role in developing the aforementioned transformation models. From a database that comprises of pairs of age separated face images of many individuals, we extract age-based facial measurements across key fiducial features and further, study textural variations across ages. We present experimental results that illustrate the applications of the proposed facial aging model in tasks such as face recognition and facial appearance prediction across aging. This thesis presents an approach to the modeling of facial aging using and extending the Morphable Model technique. For modeling the face variation across individuals, facial expressions, and physical attributes, we collected 3D face scans of 298 persons. The 3D face scans where acquired with a structured light 3D scanner, which we improved in collaboration with the manufacturer to achieve superior geometry and texture quality. Moreover, we developed an efficient way to measure fine skin structure and reflection properties with the scanner. The collected face scans have been used to build the Basel Face Model, a new publicly available Morphable Model. Using the 3D scans we learn the correlation between physical attributes such as weight, height, and especially age and faces. With the learned correlation, we present a novel way to simultaneously manipulate different attributes and demonstrate the capability to model changes caused by aging. Using the attributes of the face model in conjunction with a skull model developed in the same research group, we present a method to reconstruct faces from skull shapes which considers physical attributes, as the body weight, age etc. The most important aspect of facial aging that can not be simulated with the Morphable Model is the appearance of facial wrinkles. In this work we present a novel approach to synthesize age wrinkles based on statistics. Our wrinkle synthesis consists of two main parts: The learning of a generative model of wrinkle constellations, and the modeling of their visual appearance. For learning the constellations we use kernel density estimation of manually labeled wrinkles to estimate the wrinkle occurrence probability. To learn the visual appearance of wrinkles we use the fine scale skin structure captured with our improved scanning method. Our results show that the combination of the attribute fitting based aging and the wrinkle synthesis, facilitate a simulation of visually convincing progressive aging. The method is without restrictions applicable to any face that can be represented by the Morphable Model. Modeling the long-term facial aging process is extremely challenging due to the presence of large and non-linear variations during the face development stages. In order to efficiently address the problem, this work first decomposes the aging process into multiple short-term stages. Then, a novel generative probabilistic model, named Temporal Non-Volume Preserving (TNVP) transformation, is presented to model the facial aging process at each stage. Unlike Generative Adversarial Networks (GANs), which requires an empirical balance threshold, and Restricted Boltzmann Machines (RBM), an intractable model, our proposed TNVP approach guarantees a tractable density function, exact inference and evaluation for embedding the feature transformations between faces in consecutive stages. Our model shows its advantages not only in capturing the non-linear age related variance in each stage but also producing a smooth synthesis in age progression across faces. Our approach can model any face in the wild provided with only four basic landmark points. Moreover, the structure can be transformed into a deep convolutional network while keeping the advantages of probabilistic models with tractable log-likelihood density estimation. Our method is evaluated in both terms of synthesizing age-progressed faces and cross-age face verification and consistently shows the state-of-the-art results in various face aging databases, i.e. FG-NET, MORPH, AginG Faces in the Wild (AGFW), and Cross-Age Celebrity Dataset (CACD). A large-scale face verification on Megaface challenge 1 is also performed to further show the advantages of our proposed approach. Aging has considerable visual effects on the human face and is difficult to simulate using a universally-applicable global model. In this paper, we focus on the hypothesis that the patterns of age progression (and regression) are related to the face concerned, as the latter implicitly captures the characteristics of gender, ethnic origin, and age group, as well as possibly the person-specific development patterns of the individual. We use a data-driven framework for automatic image-based facial transformation in conjunction with a database of facial images. We build a novel parameterized model for encoding age-transformation in addition with the traditional model for face description. We utilize evolutionary computing to learn the relationship between the two models. To support this work, we also developed a new image warping algorithm based on non-uniform radial basis functions (NURBFs). Evolutionary computing was also used to handle the large parameter space associated with NURBFs. In comparison with several different methods, it consistently provides the best results against the ground truth.