ChildGAN: Face aging and rejuvenation to find missing children

Highlights：

• We present a large-scale longitudinal Indian child dataset (ICD), to address face aging, cross-age face recognition, age estimation, and gender preservation in children.

• ChildGAN, a new method for age-progression and rejuvenation, which can automatically generate visually realistic face photos, while attaining enhanced face-recognition, age-estimation, and gender-preservation rates, compared to state-of-the-art methods.

• We also investigate the generalization of ChildGAN by presenting Multi-Racial Child Dataset (MRCD) containing 64,965 face images of four races (Asian, Black, White, and Indian).

• An encoder and discriminator architecture, inspired by the self-attention GAN, which exhibits a better balance between the ability to model long-range dependencies and computational and statistical efficiency.

• The model and the MRCD web crawled images are available at: https://github.com/praveenkumarchandaliya/ChildGAN_Tamp1.

摘要

•We present a large-scale longitudinal Indian child dataset (ICD), to address face aging, cross-age face recognition, age estimation, and gender preservation in children.•ChildGAN, a new method for age-progression and rejuvenation, which can automatically generate visually realistic face photos, while attaining enhanced face-recognition, age-estimation, and gender-preservation rates, compared to state-of-the-art methods.•We also investigate the generalization of ChildGAN by presenting Multi-Racial Child Dataset (MRCD) containing 64,965 face images of four races (Asian, Black, White, and Indian).•An encoder and discriminator architecture, inspired by the self-attention GAN, which exhibits a better balance between the ability to model long-range dependencies and computational and statistical efficiency.•The model and the MRCD web crawled images are available at: https://github.com/praveenkumarchandaliya/ChildGAN_Tamp1.