A 3D Dynamic Face Recognition Method Based on Computer Vision

Abstract:

The non-rigid 3D characters recovery technology for 2D images array is affected by background diversity, motion complexity, data losing and noise of feature points, so the recognition and recovery accuracy of facial features deformation is low. Due to the high error in traditional method, the paper puts forward a 3D facial recognition algorithm based on random images array, which converts the 2D features to 3D by nonlinear mapping, and completes the recognition on foundation of 3D geometric features distance. The experimental results show that the method effectively reduces error and improves recognition effects.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 5006-5008
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.5006
• Online since: May 2014
• Authors: Bo Xia Zeng*, Wen Feng Li
• Keywords: 3D Recognition, Face Recognition, Feature Mapping

References

[1] L. Torresani, D. Yang, et. al. Tracking and modeling non-rigid objects with rank constraints[C]. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2001, 493 -500.

DOI: 10.1109/cvpr.2001.990515

[2] C. Bregler, A. Hertzmann and H. Biermann. Recovering non-rigid 3D shape from image streams[C]. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2000, 690-696.

DOI: 10.1109/cvpr.2000.854941

[3] A. Roy-Chowdhury. Towards a measure of deformability of shape sequences[J]. Pattern Recogniton Letters, 2007, vol. 28, 2164-2172.

DOI: 10.1016/j.patrec.2007.06.012

[4] X. Llado, A. Del Bue, and L. Agapito Recovering Euclidean deformable models from stereo-motion[C]. in19th International Conference on Pattern Recognition, 2008, 1-4.

DOI: 10.1109/icpr.2008.4761003

[5] J. Fayad, A. Del Bue, L. Agapito, and P. M. Q. Aguiar . Non-rigid Structure from motion using quadratic deformation models[C]. in British Machine Vision Conference (BMVC2009), London, UK, (2009).

DOI: 10.5244/c.23.110