Morales, AraceliAlomar Adrover, AntòniaPorras Pérez, Antonio ReyesLinguraru, Marius GeorgePiella Fenoy, GemmaSukno, Federico Mateo2023-07-102023-07-102023Morales A, Alomar A, Porras AR, Linguraru MG, Piella G, Sukno FM. BabyNet: reconstructing 3D faces of babies from uncalibrated photographs. Pattern Recognit. 2023;139:109367. DOI: 10.1016/j.patcog.2023.1093670031-3203http://hdl.handle.net/10230/57511We present a 3D face reconstruction system that aims at recovering the 3D facial geometry of babies from uncalibrated photographs, BabyNet. Since the 3D facial geometry of babies differs substantially from that of adults, baby-specific facial reconstruction systems are needed. BabyNet consists of two stages: 1) a 3D graph convolutional autoencoder learns a latent space of the baby 3D facial shape; and 2) a 2D encoder that maps photographs to the 3D latent space based on representative features extracted using transfer learning. In this way, using the pre-trained 3D decoder, we can recover a 3D face from 2D images. We evaluate BabyNet and show that 1) methods based on adult datasets cannot model the 3D facial geometry of babies, which proves the need for a baby-specific method, and 2) BabyNet outperforms classical model-fitting methods even when a baby-specific 3D morphable model, such as BabyFM, is used.application/pdfengThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)info:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.patcog.2023.1093673D face reconstructionGraph neural networkBaby modelinfo:eu-repo/semantics/openAccess