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@article{157909, author = {Vaishnavi M and Varshitha B S and Theekshana V and Raunak Kumari and Manasa Sandeep}, title = {A Survey on Face Age Progression Using Deep Learning}, journal = {International Journal of Innovative Research in Technology}, year = {}, volume = {9}, number = {8}, pages = {200-203}, issn = {2349-6002}, url = {https://ijirt.org/article?manuscript=157909}, abstract = {Face aging progression (FAP) refers to synthesizing facial images while simulating aging to predict a person's future appearance.The generation of age-related facial images benefits a wide range of applications, including facial recognition systems, forensic investigations, and digital entertainment. In particular, recent successes achieved with deep generative networks have significantly improved the quality of age-synthesized facial images in terms of visual fidelity, aging accuracy, and identity preservation. However, a large number of recent contributions require systematic structuring of new discoveries and ideas to identify common taxonomies, speed up future research, and reduce redundancy. FAP is translation-based, conditional-based, and sequence-based. In addition, we provide a comprehensive overview of the most common performance assessment techniques to steer future research in the right direction.}, keywords = {Divide and conquer, image translation, Face aging, Generative adversarial networks and progressive neural networks}, month = {}, }
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