Abstract
Recently 2D to 3D face reconstruction has attracted a lot of interest in the area of Computer vision. There has been a recent increase in research into various 3D reconstruction techniques using neural nets, with the majority of approaches producing high-quality results and efficiency. This paper presents an approach to convert 2D facial images to 3D and then use the 3D data and features to construct a unique digital signature. The proposed solution eliminates the need for several pictures and reduces the calculation load. The main objective of this research is to understand the progress that has already been made in this domain and we came up with an open question of whether the generation of a face mesh is possible using a Feature Vector. This would reduce the storage space required for the 3D Facial data. If Face mesh reconstruction is possible using the feature vector only, it would drastically reduce time and space for various 2D and 3D image analysis applications. The feature vector would later be compressed to create a unique digital signature. Accessing any information on the database with a key will be efficient and fast.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces, Max-Planck-Institut für biologische Kybernetik, Tübingen, Germany. IEEE Trans. Pattern Anal. Mach. Intell. 25(9) (1999)
Rehan Afzal, H.M., Luo, S., Afzal, M.K., Chaudhary, G., Khari, M., Sathish, A.P.K.: Senior member. In: IEEE 3D Face Reconstruction From Single 2D Image Using Distinctive Features. https://doi.org/10.1109/ACCESS.2020.3028106
Blanz, V., Vetter, T.: Face recognition based on fitting a 3D morphable model member, IEEE. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1063–1074 (2003)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0054760
Schumacher, M., Piotraschke, M., Blanz, V.: Hallucination of facial details from degraded images using 3D face models. Image Vis. Comput. (2015)
Egger, B., et al.: 3D Morphable Face Models-Past, Present, and Future (2019)
Zhou, S., Xiao, S.: 3D face recognition: a survey. Hum. Centric Comput. Inf. Sci. 8(1), 1–27 (2018). https://doi.org/10.1186/s13673-018-0157-2
Feng, Y., Feng, H., Black, M.J., Bolkart, T.: Learning an animatable detailed 3D face model from in-the-wild images. ACM Trans. Graph. 40 (2021)
Deng, Y., Yang, J., Xu, S., Chen, D., Jia, Y., Tong, X.: Accurate 3D face reconstruction with weakly-supervised learning, Beijing Institute of Technology From Single Image to Image Set Microsoft Research Asia Tsinghua University (2019)
Galantucci, L.M., Ferrandes, R., Percoco, G.: Digital photogrammetry for facial recognition, 6, 390–396 (2006)
Delaunay, B.: Sur la sphère vide. Bulletin de l’Académie des Sciences de l’URSS, Classe des Sciences Mathématiques et Naturelles. 6, 793–800 (1934)
Aurenhammer, F., Klein, R., Lee, D-T.: Voronoi diagrams and delaunay triangulations. World Scientific Publishing Company, p. 197 (2013). ISBN 978-981-4447-65-2
Chen, S., Zhong, S., Xue, B., Li, X., Zhao, L., Chang, C.-I.: Iterativescale-invariant feature transform for remote sensing image registration, IEEE Trans. Geosci. Remote Sens. 1–22 (2020)
Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., Pantic, M.: A semi-automatic methodology for facial landmark annotation (2013)
Huang, G.B., Ramesh, M., Berg, T., Erik, L-M.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07-49, University of Massachusetts, Amherst (2007)
Acknowledgements
I would like to express my heartfelt appreciation and gratitude to my respected mentor, Dr. Arindam Karmakar, Assistant Professor, Department of Computer Science & Engineering, Tezpur University, Assam, for his unwavering support, patience, motivation, enthusiasm, immense knowledge, and timely support and appropriate suggestions despite his hectic schedule. I would also like to express my heartfelt gratitude to all of my teachers and friends who have directly or indirectly assisted me in completing this project work. Finally, I am grateful to all of my family members, especially my parents, brother, and sister, for their moral support, timely cooperation, and encouragement in carrying out this research work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Upadhaya, A., Karmakar, A. (2023). Computing Digital Signature by Transforming 2D Image to 3D: A Geometric Perspective. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-31417-9_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-31416-2
Online ISBN: 978-3-031-31417-9
eBook Packages: Computer ScienceComputer Science (R0)