GRA-GAN: Generative adversarial network for image style transfer of Gender, Race, and age

Despite a large amount of available data, the datasets that have been recently used in studies on age estimation still entail the age class imbalance problem owing to different age distributions of race or gender. This results in overfitting in which training data aligns toward one side and ultimately reduces the generality of age estimation. Same problems can occur in the cases of race and gender recognition. This problem can be solved if age images that were insufficient in a previously trained distribution or race and gender information that was not considered in the previously trained distribution can be newly created as images that are identical to the previously trained distribution. Therefore, we propose a race, age, and gender image transformation technique by a generative adversarial network for image style transfer of gender, race, and age (GRA-GAN) based on channel-wise and multiplication-based information fusion of encoder and decoder features. Experiments using four open databases (MORPH, AAF, AFAD, and UTK) indicated that our method outperformed the state-of-the-art methods.