Abstract
Face reconstruction becomes more accurate and popular in the age of artificial intelligence and deep learning. Particularly, 3-D face reconstruction can be one of the key technologies in meta-world and virtual reality applications. In recent years, studies on realistic 3-D face reconstruction draw much attention among the researchers. For realizing accurate shape and facial textures, the neural network models for deep learning are under investigation and several schemes are present-ed. In this paper, a deep learning based simulated annealing algorithm is proposed for 3-D face reconstruction. Face labeling, feature extraction, and 3-D reconstruction are three major elements investigated in this study. A set of computer simulation is performed by using the CelebFaces Attributes and Labeled Faces in the Wild data sets. The system performance is evaluated in terms of reconstruction accuracy and the results show us that the proposed method can be a successful alternative for providing accurate and robust 3-D face reconstruction.
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Chen, F.F., Guan, B., Kim, S., Choi, J. (2023). 3-D Face Reconstruction Method Using Deep Learning Based Simulated Annealing. In: Kahraman, C., Sari, I.U., Oztaysi, B., Cebi, S., Cevik Onar, S., Tolga, A.Ç. (eds) Intelligent and Fuzzy Systems. INFUS 2023. Lecture Notes in Networks and Systems, vol 759. Springer, Cham. https://doi.org/10.1007/978-3-031-39777-6_26
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DOI: https://doi.org/10.1007/978-3-031-39777-6_26
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