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.
use the age prototypes to synthesize new face images. The average faces of people in the same age group are used as the prototypes @cite_14 . The input image can be transformed into the age-progressed face by adding the differences between the prototypes of two age groups @cite_12 . Recently, Kemelmacher- @cite_1 proposed to construct sharper average prototype faces from a large-scale set of images in combining with subspace alignment and illumination normalization.
@cite_14 @cite_1 @cite_12
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A technique for defining facial prototypes is described which supports transformations along quantifiable dimensions in "face space". Examples illustrate the use of shape and color information to perform predictive gender and age transformations. The processes we describe begin with the creation of a facial prototype. Generally, a prototype can be defined as a representation containing the consistent attributes across a class of objects. Once we obtain a class prototype, we can take an exemplar that has some information missing and augment it with the prototypical information. In effect, this "adds in" the average values for the missing information. We use this notion to transform gray-scale images into full color by including the color information from a relevant prototype. It is also possible to deduce the difference between two groups within a class. Separate prototypes can be formed for each group. These can be used subsequently to define a transformation that will map instances from one group onto the domain of the other. This paper details the procedure we use to transform facial images and shows how it can be used to alter perceived facial attributes. > This study investigated visual cues to age by using facial composites which blend shape and colour information from multiple faces. Baseline measurements showed that perceived age of adult male faces is on average an accurate index of their chronological age over the age range 20-60 years. Composite images were made from multiple images of different faces by averaging face shape and then blending red, green and blue intensity (RGB colour) across comparable pixels. The perceived age of these composite or blended images depended on the age bracket of the component faces. Blended faces were, however, rated younger than their component faces, a trend that became more marked with increased component age. The techniques used provide an empirical definition of facial changes with age that are biologically consistent across a sample population. The perceived age of a blend of old faces was increased by exaggerating the RGB colour differences of each pixel relative to a blend of young faces. This effect on perceived age was not attributable to enhanced contrast or colour saturation. Age-related visual cues defined from the differences between blends of young and old faces were applied to individual faces. These transformations increased perceived age.