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However, no one has tackled the problem of predicting one of the parents using the other parent and their children or answering the question of the possibility of deducing the parent images from the children and other parent image features. It could be used in parental identification cases. Moreover, it could help children who don\u2019t know one of their parents to have a visual representation of their images. To perform this task, several obstacles were overcome, like the small number of parent pairs in the dataset and stabilizing the GANs to produce good-looking images. The proposed method depends on dual GAN architecture in addition to adaptive instance normalization layers and introducing a triple loss function to stabilize further and improve the resulting images. The results were proven using a kinship verification model, a face verification model, and other well-known evaluation metrics, which showed that the generated parent images are of decent quality compared to real parents\u2019 images with affordable computational hardware. As a result, a novel method is developed that could produce unknown parent images.<\/jats:p>","DOI":"10.1007\/s11042-024-20186-y","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T07:01:32Z","timestamp":1727766092000},"page":"28643-28665","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Parent GAN: image generation model for creating parent\u2019s images using children\u2019s images"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9247-9722","authenticated-orcid":false,"given":"M. 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