Abstract
Face recognition in unconstrained environments is often influenced by pose variations. And the problem is basically the identification that uses partial data. In this paper, a method fusing structure and texture information is proposed to solve the problem. In the register phase, the approximate 180 degree information of face is acquired, and the data used to identify individual is obtained from a random single view. Pure face is extracted from 3D data first, then convert the original data to the form of spherical depth map (SDM) and spherical texture map (STM), which are invariant to out-plane rotation, subsequently facilitating the successive alignment-free identification that is robust to pose variations. We make identification through sparse representation for its well performance with the two maps. Experiments show that our proposed method gets a high recognition rate with pose and expression variations.
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
Preview
Unable to display preview. Download preview PDF.
References
Jain, A.K., Flynn, P., Ross, A.A. (eds.).: Handbook of biometrics, Springer, US (2007)
Zhang, H., Zhang, Y., Huang, T.S.: Pose-robust face recognition via sparse representation. Pattern Recognition 46(5), 1511–1521 (2013)
Nixon, M.S., Bouchrika, I., Arbab-Zavar, B., Carter, J.N.: On use of biometrics in forensics: Gait and ear. In: European Signal Processing Conference (2010)
Liu, X., Chen, T.: Pose-robust face recognition using geometry assisted probabilistic modeling. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1:502–509. IEEE (2005)
Singh, R., Vatsa, M., Ross, A.: A mosaicking scheme for pose-invariant face recognition. IEEE Transactions Systems, Man, and Cybernetics, Part B: Cybernetics 37(5), 1212–1225 (2007)
Yang, F., Paindavoine, M., Abdi, H., Monopoli, A.: Development of a fast panoramic face mosaicking and recognition system. Optical Engineering, 44(8) (2005)
Rama, A., Tarres, F., Rurainsky, J.: Aligned texture map creation for pose invariant face recognition. Multimedia Tools and Applications 49(3), 545–565 (2010)
Lei, Y., Bennamoun, M., El-Sallam, A.A.: An efficient 3D face recognition approach based on the fusion of novel local low-level features. Pattern Recognition 46(1), 24–37 (2013)
Mohammadzade, H., Hatzinakos, D.: Iterative Closest Normal Point for 3D Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(2), 381–397 (2013)
Huang, D., Ardabiliab, M., Wang, Y., Chen, L.: Asymmetric 3D/2D face recognition based on LBP facial representation and canonical correlation analysis. In: 16th IEEE International Conference on Image Processing (ICIP), pp.3325–3328. IEEE (2009)
Dorai, C., Jain, A.K.: COSMOS-A representation scheme for 3d free-form objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(10), 1115–1130 (1997)
Liu, P., Wang, Y., Zhang, Z.: Representing 3D face from point cloud to face-aligned spherical depth map. International Journal of Pattern Recognition and Artificial Intelligence, 26(1) (2012)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2), 210–227 (2009)
Liao, S., Jain, A.K., Li, S.Z.: Partial face recognition: Alignment-free approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(5), 1193–1205 (2013)
Yu, G., Morel, J.M.: ASIFT: An Algorithm for Fully Affine Invariant Comparison, Image Processing On Line. 1 (2011)
Donoho, D.L., Tsaig, Y.: Fast Solution of L1-Norm Minimization Problems When the Solution May be Sparse. IEEE Transactions on Information Theory 54(11), 4789–4812 (2008)
Zhang, B., Mu, Z., Li, C., Zeng, H.: Robust classification for occluded ear via Gabor scale feature-based non-negative sparse representation. Optical Engineering, 53(6) (2014)
Besl, P.J., Mckay, N.D.: A method for registration of 3-D shape. IEEE Transaction on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Liu, S., Mu, Z., Huang, H. (2015). 3D Face Recognition Fusing Spherical Depth Map and Spherical Texture Map. In: Yang, J., Yang, J., Sun, Z., Shan, S., Zheng, W., Feng, J. (eds) Biometric Recognition. CCBR 2015. Lecture Notes in Computer Science(), vol 9428. Springer, Cham. https://doi.org/10.1007/978-3-319-25417-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-25417-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25416-6
Online ISBN: 978-3-319-25417-3
eBook Packages: Computer ScienceComputer Science (R0)