[1] Fu, Y., Guo, G., & Huang, T. S., Age synthesis and estimation via faces: A survey. IEEE transactions on pattern analysis and machine intelligence, 32(11), 1955-1976.Diederik P Kingma and Max Welling, “Auto- encoding variational bayes,” in Proceedings of International Conference on Learning Representations, Banff, Canada, 2014 (2010).[2] Ramanathan, N., & Chellappa, R., Face verification across age progression. IEEE transactions on image processing, 15(11), 3349-3361 (2006).[3] Amos, B., Ludwiczuk, B., & Satyanarayanan, M., Openface: A general-purpose face recognition library with mobile applications. CMU School of Computer Science, 6(2), 20 (2016). [4] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y., Generative adversarial nets. Advances in Neural Information Processing Systems, 27 (2014).[5]  Wang, Z., Tang, X., Luo, W., & Gao, S., Face aging with identity-preserved conditional generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7939-7947 (2018).[6] Fang, H., Deng, W., Zhong, Y., & Hu, J., Triple-GAN: Progressive face aging with triple translation loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 804-805 (2020).[7] Perarnau, G., Van De Weijer, J., Raducanu, B., & Álvarez, J. M., Invertible conditional gans for image editing (2016). arXiv preprint arXiv:1611.06355.[8]  Antipov, G., Baccouche, M., Berrani, S. A., & Dugelay, J. L., Apparent age estimation from face images combining general and children-specialized deep learning models. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 96-104 (2016).[9] Amos, B., Ludwiczuk, B., & Satyanarayanan, M., Openface: A general-purpose face recognition library with mobile applications. CMU School of Computer Science, 6(2), 20 (2016).[10]  Kingma, D. P., & Welling, M., Auto-encoding variational bayes (2013). arXiv preprint arXiv:1312.6114.[11]  Schroff, F., Kalenichenko, D., & Philbin, J., Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 815-823 (2015).[12] Ling, H., Soatto, S., Ramanathan, N., & Jacobs, D. W., Face verification across age progression using discriminative methods. IEEE Transactions on Information Forensics and Security, 5(1), 82-91 (2009).[13] Xu, C., Tang, Y., Toyoura, M., Xu, J., & Mao, X., Generating users’ desired face image using the conditional generative adversarial network and relevance feedback. IEEE Access, 7, 181458-181468 (2019).[14] Xu, C., Tang, Y., Toyoura, M., Xu, J., & Mao, X., Generating users’ desired face image using the conditional generative adversarial network and relevance feedback. IEEE Access, 7, 181458-181468 (2019).[15] Grimmer, M., Ramachandra, R., & Busch, C., Deep face age progression: A survey. IEEE Access, 9, 83376-83393 (2021).[16] Ramanathan, N., & Chellappa, R., Modeling age progression in young faces. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), Vol. 1, pp. 387-394 (2006, June). IEEE.[17] Zhang, Z., Song, Y., & Qi, H., Age progression/regression by  conditional adversarial autoencoder. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5810-5818 (2017).[18] Fu, Y., Guo, G., & Huang, T. S., Age synthesis and estimation via faces: A survey. IEEE transactions on pattern analysis and machine intelligence, 32(11), 1955-1976 (2010).