Scale-Space Volume Descriptors for Automatic 3D Facial Feature Extraction
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:50847", author = "Daniel Chen and George Mamic and Clinton Fookes and Sridha Sridharan", title = "Scale-Space Volume Descriptors for Automatic 3D Facial Feature Extraction", 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", volume = "3", number = "11", pages = "2510-6", }
