Learning Face Age Progression: A Pyramid Architecture of GANs

The two underlying requirements of face age progression, i.e. aging accuracy and identity permanence, are not well studied in the literature. In this paper, we present a novel generative adversarial network based approach. It separately models the constraints for the intrinsic subject-specific characteristics and the age-specific facial changes with respect to the elapsed time, ensuring that the generated faces present desired aging effects while simultaneously keeping personalized properties stable. Further, to generate more lifelike facial details, high-level age-specific features conveyed by the synthesized face are estimated by a pyramidal adversarial discriminator at multiple scales, which simulates the aging effects in a finer manner. The proposed method is applicable to diverse face samples in the presence of variations in pose, expression, makeup, etc., and remarkably vivid aging effects are achieved. Both visual fidelity and quantitative evaluations show that the approach advances the state-of-the-art.Comment: CVPR 2018. V4 and V2 are the same, i.e. the conference version; V3 is a related but different work, which is mistakenly submitted and will be submitted as a new arXiv pape

Playful expressions of one-year-old chimpanzee infants in social and solitary play contexts

Knowledge of the context and development of playful expressions in chimpanzees is limited because research has tended to focus on social play, on older subjects, and on the communicative signaling function of expressions. Here we explore the rate of playful facial and body expressions in solitary and social play, changes from 12- to 15-months of age, and the extent to which social partners match expressions, which may illuminate a route through which context influences expression. Naturalistic observations of seven chimpanzee infants (Pan troglodytes) were conducted at Chester Zoo, UK (n = 4), and Primate Research Institute, Japan (n = 3), and at two ages, 12 months and 15 months. No group or age differences were found in the rate of infant playful expressions. However, modalities of playful expression varied with type of play: in social play, the rate of play faces was high, whereas in solitary play, the rate of body expressions was high. Among the most frequent types of play, mild contact social play had the highest rates of play faces and multi-modal expressions (often play faces with hitting). Social partners matched both infant play faces and infant body expressions, but play faces were matched at a significantly higher rate that increased with age. Matched expression rates were highest when playing with peers despite infant expressiveness being highest when playing with older chimpanzees. Given that playful expressions emerge early in life and continue to occur in solitary contexts through the second year of life, we suggest that the play face and certain body behaviors are emotional expressions of joy, and that such expressions develop additional social functions through interactions with peers and older social partners

Age Progression/Regression by Conditional Adversarial Autoencoder

"If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not necessarily paired), can you show me what I would look like when I am 80 or what I was like when I was 5?" The answer is probably a "No." Most existing face aging works attempt to learn the transformation between age groups and thus would require the paired samples as well as the labeled query image. In this paper, we look at the problem from a generative modeling perspective such that no paired samples is required. In addition, given an unlabeled image, the generative model can directly produce the image with desired age attribute. We propose a conditional adversarial autoencoder (CAAE) that learns a face manifold, traversing on which smooth age progression and regression can be realized simultaneously. In CAAE, the face is first mapped to a latent vector through a convolutional encoder, and then the vector is projected to the face manifold conditional on age through a deconvolutional generator. The latent vector preserves personalized face features (i.e., personality) and the age condition controls progression vs. regression. Two adversarial networks are imposed on the encoder and generator, respectively, forcing to generate more photo-realistic faces. Experimental results demonstrate the appealing performance and flexibility of the proposed framework by comparing with the state-of-the-art and ground truth.Comment: Accepted by The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017