Quantitative evaluations by aĬOTS face recognition system demonstrate that the target age distributions areĪccurately recovered, and 99.88% and 99.98% age progressed faces can beĬorrectly verified at 0.001% FAR after age transformations of approximately 28Īnd 23 years elapsed time on the MORPH and CACD databases, respectively. Related Work 2. We are able to build conditional deep convolutional neural networks that achieve convincing results. Using Generative Adversarial Networks, we seek to generate older versions of oneself while preserving the identity of the individual. The proposed method isĪpplicable even in the presence of variations in pose, expression, makeup,Įtc., achieving remarkably vivid aging effects. we will only consider age progression rather than age regression. Resulting in smooth continuous face aging sequences. Simultaneously train a single generator and multiple parallel discriminators, Further, an adversarial learning scheme is introduced to To ensure photo-realistic facial details, high-level age-specificįeatures conveyed by the synthesized face are estimated by a pyramidalĪdversarial discriminator at multiple scales, which simulates the aging effects With respect to the elapsed time, ensuring that the generated faces presentĭesired aging effects while simultaneously keeping personalized properties Intrinsic subject-specific characteristics and the age-specific facial changes It separately models the constraints for the Presents a novel generative adversarial network based approach to address the This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images longitudinal spans from a few. face modeling, photo-realistic animation, face recognition, etc. aging accuracyĪnd identity permanence, are not well studied in the literature. This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. Download a PDF of the paper titled Learning Continuous Face Age Progression: A Pyramid of GANs, by Hongyu Yang and 3 other authors Download PDF Abstract: The two underlying requirements of face age progression, i.e.
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