LAUSR

The use of computer simulation to understand how human faces age has been a growing area of research since decades. It has been applied to the search for missing children as well as to the fields of entertainment, cosmetics and dermatology research. Our objective is to elaborate a model for the age-related changes of visual cues which affect the perception of age, so that we may better predict them. Traditional approaches based on the Active Appearance Model (AAM) tend to blurry appearance and wipe out texture details such as wrinkles. We introduce Wrinkle Oriented Active Appearance Model (WOAAM) where a new channel is added to the AAM dedicated to analyze wrinkles. Firstly, we propose to represent both the shape and texture of each wrinkle on a face by a compact and interpretable vector. Afterwards, to model the distribution of wrinkles on a face, we introduce a new way to approximate an empiric joint probability density by creating an ensemble of joint probability densities estimated by Kernel Density Estimation. Finally, we show how to create new samples from such an ensemble of densities, and thus synthesize new plausible wrinkles. In comparison to other methods which add wrinkles at post-processing level, our method fully integrates them in AAM. Thereby, the wrinkles generated are statistically representative of a specific age in terms of number, length, shape and intensity. With an age estimation Convolutional Neural Network, we found that age-progressed faces produced by the WOAAM better reduces the gap between the expected age and the estimated age than those produced by a classic AAM.