![]() ![]() ![]() This could lead to improvements in biometric systems, assist in the search for missing people and in the identification of criminals in an automated way. These networks can encode latent information, by enabling a generator model to create new images conditional on an input image. With them, it becomes possible to generate realistically aged faces of specific individuals. With advances in computer vision, generative models have been applied to perform this task, especially generative adversarial networks (GANs). Both approaches fail to keep individual characteristics when transforming a face from a younger domain to an aged one. The first calculates the mean difference between age groups, and the latter uses parametric models to simulate change over time. Traditionally, there have been two kinds of modeling techniques used in this task: prototype-based and model-based methods. When computationally aging a face, it is desirable that the age output is close to the expected age and the individual’s characteristics are maintained.
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