Abstract
The frontal facial synthesis results of current main methods tend to be smooth and lack personal characteristics, which greatly influence the subjective impression. Especially aiming at large-scale deflecting faces, the shielding side just provides few features for reconstructing and the deformation of facial elements enhance the difficulty to obtain exact features of target frontal face, making the synthesis result seem to be same and as mean face of database. In this paper, to solve these problems, we propose a novel two-step face synthesis method. In the first step, we utilize the basic symmetry of human face to predict the missing patches according to the other side and generate interim facial image. And in the following step, we introduce the individual residual facial image between interim result and mean face to compensate for the lost personal features of the synthesis result because the residual image carries more individual characteristic of the input face. We show that experimental results of proposed method outperform in the objective and subjective effects with other state-of-the-art methods.
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Vetter, T., Poggio, T.: Linear object classes and image synthesis from a single example image. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 733–742 (1995)
Ding, C., Tao, D.: A comprehensive survey on pose-invariant face recognition. ACM Trans. Intell. Syst. Technol. 7(3) (2015)
Lee, M.W., Ranganath, S.: Pose-invariant face recognition using a 3D deformable model. Pattern Recogn. 36(8), 1835–1846 (2003)
Zhang, X., Gao, Y., Zhang, B.-L.: Recognizing rotated faces from two orthogonal views in mugshot databases, vol. 1, pp. 195–198 (2006)
Aldrian, O., Smith, W.A.P.: Inverse rendering of faces with a 3D morphable model. IEEE Trans. Pattern Anal. Mach. Intell. 35(5), 1080–1093 (2013)
Blanz, V., Vetter, T.: A morphable model for the synthesis of 3D faces. In: Conference on Computer Graphics and Interactive Techniques, pp. 187–194 (1999)
Blanz, V., Vetter, T.: Face recognition based on fitting a 3D morphable model. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1063–1074 (2003)
Zhu, X., Lei, Z., Yan, J., Yi, D.: High-fidelity pose and expression normalization for face recognition in the wild, pp. 787–796 (2015)
Hassner, T.: Viewing real-world faces in 3D. In: International Conference on Computer Vision (ICCV), December 2013
Zhu, X., Lei, Z., Liu, X., Shi, H., Li, S.Z.: Face alignment across large poses: a 3D solution. Computer Science (2015)
Vetter, T., Jones, M.J., Poggio, T.: A bootstrapping algorithm for learning linear models of object classes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 40–46 (1997)
Chai, X., Shan, S., Chen, X., Gao, W.: Locally linear regression for pose-invariant face recognition. IEEE Trans. Image Process. 16(7), 1716–1725 (2007)
Han, Z., Jiang, J., Hu, R., Lu, T.: A novel frontal view synthesis method based on neighbor embedding. In: International Conference on Image Analysis and Signal Processing, pp. 128–132 (2011)
Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 275–282 (2004)
Hao, Y., Qi, C.: A unified regularization framework for virtual frontal face image synthesis. IEEE Sig. Process. Lett. 22(22), 559–563 (2015)
Wei, X., Hu, R., Han, Z., Chen, L., Ding, X.: A stepwise frontal face synthesis approach for large pose non-frontal facial image. In: Chen, E., Gong, Y., Tie, Y. (eds.) PCM 2016. LNCS, vol. 9917, pp. 439–448. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48896-7_43
Jiang, J., Ruimin, H., Wang, Z., Han, Z.: Noise robust face hallucination via locality-constrained representation. IEEE Trans. Multimedia 16(5), 1268–1281 (2014)
Acknowledgments
The research was supported by the National High Technology Research and Development Program of China (863 Program) No. 2015AA-016306, National Nature Science Foundation of China (61231015, 61671336, 61671332), the EU FP7 QUICK project under Grant Agreement No. PIRSES-GA-2013-612652*, Hubei Province Technological Innovation Major Project (No. 2016AAA015, 2017AAA123), Natural Science Foundation of Hubei Province (2016CFB573).
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Ding, X., Hu, R., Han, Z., Wang, Z. (2018). A Novel Frontal Facial Synthesis Algorithm Based on Individual Residual Face. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10705. Springer, Cham. https://doi.org/10.1007/978-3-319-73600-6_2
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DOI: https://doi.org/10.1007/978-3-319-73600-6_2
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