Abstract
In recent years, many 3D face reconstruction from image methods have been introduced and most of them have shown incredible results. However, methods such as photogrammetry require images from multiple views and can be very time-consuming. Deep learning based methods, on the other hand, are faster and more efficient but heavily rely on the base face models and training datasets. Meanwhile, most base face models lack Asian facial features, and high-quality Vietnamese facial image databases are still not available yet. In this paper, we propose an approach that increases the accuracy of Vietnamese 3D faces generated from a single image by creating a new mean face shape and training a convolution neural network with our dataset. This method is compact and can improve the quality of 3D face reconstruction using facial image data with specific geographical and race characteristics.
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References
Basel face model - details. https://faces.dmi.unibas.ch/bfm/main.php?nav=1-1-0 &id=details Accessed 22 Jul 2022
Amberg, B., Romdhani, S., Vetter, T.: Optimal step nonrigid ICP algorithms for surface registration. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007). https://doi.org/10.1109/CVPR.2007.383165
Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 187–194. SIGGRAPH 1999, ACM Press/Addison-Wesley Publishing Co., USA (1999). https://doi.org/10.1145/311535.311556
Booth, J., Roussos, A., Zafeiriou, S., Ponniah, A., Dunaway, D.: A 3D morphable model learnt from 10,000 faces. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5543–5552 (2016). https://doi.org/10.1109/CVPR.2016.598
Cao, C., Weng, Y., Zhou, S., Tong, Y., Zhou, K.: Facewarehouse: A 3D facial expression database for visual computing. IEEE Trans. Visual Comput. Graphics 20(3), 413–425 (2014). https://doi.org/10.1109/TVCG.2013.249
Dai, H., Pears, N., Smith, W., Duncan, C.: A 3D morphable model of craniofacial shape and texture variation. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3104–3112 (2017). https://doi.org/10.1109/ICCV.2017.335
Dai, H., Pears, N., Smith, W., Duncan, C.: Statistical modeling of craniofacial shape and texture. Int. J. Comput. Vision 128(2), 547–571 (2019). https://doi.org/10.1007/s11263-019-01260-7
Deng, Y., Yang, J., Xu, S., Chen, D., Jia, Y., Tong, X.: Accurate 3D face reconstruction with weakly-supervised learning: from single image to image set. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 285–295 (2019). https://doi.org/10.1109/CVPRW.2019.00038
Dutta, K., Bhattacharjee, D., Nasipuri, M.: SpPCANet: a simple deep learning-based feature extraction approach for 3D face recognition. Multimedia Tools Appl. 79(41–42), 31329–31352 (2020). https://doi.org/10.1007/s11042-020-09554-6
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(4), 1–13 (2021). https://doi.org/10.1145/3450626.3459936
Gerig, T., et al.: Morphable face models - an open framework. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 75–82 (2018). https://doi.org/10.1109/FG.2018.00021
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90
Li, T., Bolkart, T., Black, M.J., Li, H., Romero, J.: Learning a model of facial shape and expression from 4D scans. ACM Trans. Graph. 36(6), 1–17 (2017). https://doi.org/10.1145/3130800.3130813
Paysan, P., Knothe, R., Amberg, B., Romdhani, S., Vetter, T.: A 3D face model for pose and illumination invariant face recognition. In: 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 296–301 (2009). https://doi.org/10.1109/AVSS.2009.58
Ploumpis, S., et al.: Towards a complete 3D morphable model of the human head. IEEE Trans. Pattern Anal. Mach. Intell. 43(11), 4142–4160 (2021). https://doi.org/10.1109/TPAMI.2020.2991150
Ploumpis, S., Wang, H., Pears, N., Smith, W.A.P., Zafeiriou, S.: Combining 3D morphable models: a large scale face-and-head model. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10926–10935 (2019). https://doi.org/10.1109/CVPR.2019.01119
Richard, A., Zollhöfer, M., Wen, Y., de la Torre, F., Sheikh, Y.: MeshTalk: 3D face animation from speech using cross-modality disentanglement. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1153–1162 (2021). https://doi.org/10.1109/ICCV48922.2021.00121
Sanyal, S., Bolkart, T., Feng, H., Black, M.J.: Learning to regress 3D face shape and expression from an image without 3D supervision. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7755–7764 (2019). https://doi.org/10.1109/CVPR.2019.00795
Thies, J., Zollhöfer, M., Stamminger, M., Theobalt, C., Nießner, M.: Face2face: Real-time face capture and reenactment of RGB videos. Commun. ACM 62(1), 96–104 (2018). https://doi.org/10.1145/3292039
Xu, K., Wang, X., Hu, Z., Zhang, Z.: 3D face recognition based on twin neural network combining deep map and texture. In: 2019 IEEE 19th International Conference on Communication Technology (ICCT), pp. 1665–1668 (2019). https://doi.org/10.1109/ICCT46805.2019.8947113
Xu, S., et al.: Deep 3D portrait from a single image. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7707–7717 (2020). https://doi.org/10.1109/CVPR42600.2020.00773
Zaidi, A.A., et al.: Investigating the case of human nose shape and climate adaptation. PLoS Genet. 13(3), 1–31 (2017). https://doi.org/10.1371/journal.pgen.1006616
Zielonka, W., Bolkart, T., Thies, J.: Towards metrical reconstruction of human faces. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision–ECCV 2022. LNCS, vol. 13673. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19778-9_15
Acknowledgements
This work is funded by Vietnam - Korea Institute of Science and Technology, Ministry of Science and Technology. We would like to express our deepest appreciation to the Vietnam Ministry of Science and Technology for supporting us with the 02.M02.2022 project.
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Nguyen, DH., Han Tien, KA., Ma, TC., Nguyen The, HA. (2022). A New 3D Face Model for Vietnamese Based on Basel Face Model. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_33
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