Scale-Space Volume Descriptors for Automatic 3D Facial Feature Extraction

Authors: Daniel Chen, George Mamic, Clinton Fookes, Sridha Sridharan

Abstract:

An automatic method for the extraction of feature points for face based applications is proposed. The system is based upon volumetric feature descriptors, which in this paper has been extended to incorporate scale space. The method is robust to noise and has the ability to extract local and holistic features simultaneously from faces stored in a database. Extracted features are stable over a range of faces, with results indicating that in terms of intra-ID variability, the technique has the ability to outperform manual landmarking.

Keywords: Scale space volume descriptor, feature extraction, 3D facial landmarking

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1055711

References:

[1] P. Phillips, P. Flynn, T. Scruggs, K. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, "Overview of the face recognition grand challenge," in Proceedings IEEE Conference on Computer Vision and Pattern Recognition, 2005, pp. 947-954.
[2] T. J. Hutton, B. F. Buxton, and P. Hammond, "Dense surface point distribution models of the human face," in Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, 2001, pp. 153- 160.
[3] D. Rueckert, A. F. Frangi, and J. A. Schnabel, "Automatic construction of 3d statistical deformation models using non-rigid registration," IEEE Transactions on Medical Imaging, vol. 22, pp. 1014-1025, 2003.
[4] V. Blanz and T. Vetter, "Face recognition based on fitting a 3d morphable model," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, pp. 1063-1074, 2003.
[5] Y. Wang, B. S. Peterson, and L. H. Staib, "Shape-based 3d surface correspondence using geodesics and local geometry," in Proceedings IEEE Conference on Computer Vision and Pattern Recognition, 2000, pp. 644-651.
[6] A. D. Brett, A. Hill, and C. J. Taylor, "A method of 3d surface correspondence for automated landmark generation," in British Machine Vision Conference, 1997, pp. 709-718.
[7] S. Manay, B.-W. Hong, and A. J. Yezzi, "Integral invariant signatures," in Proceedings 8th European Conference on Computer Vision, 2004, pp. 87-99.
[8] N. Gelfand, N. J. Mitra, L. J. Guibas, and H. Pottmann, "Robust global registration," in Proceedings 3rd Eurographics Symposium on Geometry Processing, 2005, pp. 197-206.
[9] C. Fookes, G. Mamic, C. McCool, and S. Sridharan, "Normalisation and recognition of 3d face data using robust hausdorff metric," in Proceedings Digital Image Computing: Techniques and Applications, 2008, pp. 124-129.
[10] F. Steinke, B. Sch¨olkopf, and V. Blanz, "Learning dense 3d correspondence," in Proceedings 20th Annual Conference on Neural Information Processing Systems, 2006.