Abstract
This paper presents a reliable and robust approach for 3D face reconstruction using low-cost depth cameras. The solution is designed to withstand both camera noise and motion artifacts, making it potentially suitable for dealing with living subjects. Our method utilizes Iterative Closest Point (ICP) registration with a calibration pattern, allowing for scalable acquisition with multiple devices. Experiments are conducted adopting two low-cost depth cameras, and producing the 3D reconstruction of a dummy head to favour a metrological evaluation. The findings indicate that the suggested approach outperforms the alternative method of directly applying ICP to the facial point cloud. Additionally, the outcomes demonstrate that the low-cost solution deviates from the high-quality professional equipment by an average of 0.5 mm, showing the notable accuracy of the proposed method.
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References
Li, M., Huang, B., Tian, G.: A comprehensive survey on 3D face recognition methods. Eng. Appl. Artif. Intell. 110, 104669 (2022)
Grossi, G., Lanzarotti, R., Lin, J.: Robust face recognition providing the identity and its reliability degree combining sparse representation and multiple features. Int. J. Pattern Recognit Artif Intell. 30(10), 1656007 (2016)
Sandbach, G., Zafeiriou, S., Pantic, M., Yin, L.: Static and dynamic 3D facial expression recognition: a comprehensive survey. Image Vis. Comput. 30(10), 683–697 (2012)
Cuculo, V., D’Amelio, A.: OpenFACS: an open source FACS-based 3D face animation system. In: Zhao, Y., Barnes, N., Chen, B., Westermann, R., Kong, X., Lin, C. (eds.) ICIG 2019. LNCS, vol. 11902, pp. 232–242. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-34110-7_20
Hammond, P., et al.: The use of 3D face shape modelling in dysmorphology. Arch. Dis. Child. 92(12), 1120 (2007)
Hallgrímsson, B., et al.: Automated syndrome diagnosis by three-dimensional facial imaging. Genet. Med. 22(10), 1682–1693 (2020)
Katz, D.C., et al.: Facial shape and allometry quantitative trait locus intervals in the diversity outbred mouse are enriched for known skeletal and facial development genes. PLoS ONE 15(6), e0233377 (2020)
Dolci, C., Sansone, V.A., Gibelli, D., Cappella, A., Sforza, C.: Distinctive facial features in Andersen-tawil syndrome: a three-dimensional stereophotogrammetric analysis. Am. J. Med. Genet. A 185(3), 781–789 (2021)
Cao, Y., Liu, S., Zhao, P., Zhu, H.: RP-NET: a PointNet++ 3D face recognition algorithm integrating RoPS local descriptor. IEEE Access 10, 91245–91252 (2022)
Zhang, J., Gao, K., Fu, K., Cheng, P.: Deep 3D facial landmark localization on position maps. Neurocomputing 406, 89–98 (2020)
Kim, D., Hernandez, M., Choi, J., Medioni, G.: Deep 3D face identification. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 133–142. IEEE (2017)
Gibelli, D., Dolci, C., Cappella, A., Sforza, C.: Reliability of optical devices for three-dimensional facial anatomy description: a systematic review and meta-analysis. Int. J. Oral Maxillofac. Surg. 49(8), 1092–1106 (2020)
Peng, H., Yang, L., Li, J.: Robust and high-fidelity 3D face reconstruction using multiple RGB-d cameras. Appl. Sci. 12(22), 11722 (2022)
Intel RealSense Product Family D400 Series, rev. 015. Intel Corp. (2023). https://www.intelrealsense.com/support/
Canfield Scientific. VECTRA M3 3D Imaging System (2023). https://www.canfieldsci.com/imaging-systems/vectra-m3-3d-imaging-system/
Lou, J., et al.: Realistic facial expression reconstruction for VR HMD users. IEEE Trans. Multimedia 22(3), 730–743 (2019)
Song, S.L., Shi, W., Reed, M.: Accurate face rig approximation with deep differential subspace reconstruction. ACM Transactions on Graphics (TOG) 39(4), 34–1 (2020)
Jiang, L., Zhang, J., Deng, B., Li, H., Liu, L.: 3D face reconstruction with geometry details from a single image. IEEE Trans. Image Process. 27(10), 4756–4770 (2018)
Wang, X., Guo, Y., Yang, Z., Zhang, J.: Prior-guided multi-view 3D head reconstruction. IEEE Trans. Multimedia 24, 4028–4040 (2021)
Yao, Y., Luo, Z., Li, S., Fang, T., Quan, L.: MVSNet: depth inference for unstructured multi-view stereo. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 767–783 (2018)
Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-D point sets. IEEE Trans. Pattern Anal. Mach. Intell. 5, 698–700 (1987)
Amor, B.B., Ardabilian, M., Chen, L.: New experiments on ICP-based 3D face recognition and authentication. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 3, pp. 1195–1199. IEEE (2006)
Wang, C.-W., Peng, C.-C.: 3D face point cloud reconstruction and recognition using depth sensor. Sensors 21(8), 2587 (2021)
Fukushima, N.: ICP with depth compensation for calibration of multiple ToF sensors. In: 2018–3DTV-Conference: The True Vision-Capture, Transmission and Display of 3D Video (3DTV-CON), pp. 1–4. IEEE (2018)
Kazhdan, M., Hoppe, H.: Screened poisson surface reconstruction. ACM Trans. Graph. 32, 3 (2013). https://doi.org/10.1145/2487228.2487237
Acknowledgment
We would like to acknowledge the Laboratory of Anatomy of the Stomatognathic System at the Department of Biomedical Sciences for Health, University of Milan, for providing us with access to the images obtained using the VECTRA M3 3D Imaging System.
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Burger, J., Facchi, G., Grossi, G., Lanzarotti, R., Pedersini, F., Tartaglia, G. (2023). Extrinsic Calibration of Multiple Depth Cameras for 3D Face Reconstruction. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing – ICIAP 2023. ICIAP 2023. Lecture Notes in Computer Science, vol 14234. Springer, Cham. https://doi.org/10.1007/978-3-031-43153-1_30
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