Translator Disclaimer
5 May 2000 Extracting invariant features of the human face from 3D range data
Author Affiliations +
Proceedings Volume 3905, 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making; (2000) https://doi.org/10.1117/12.384873
Event: 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, 1999, Washington, DC, United States
Abstract
The surface of the human face can be represented by a set of facets. The Phase Fourier Transform (PFT) can be used to transform a facet in the space domain to a peak in the frequency domain. The position and the distribution of the peak represent the orientation and shape of the facet respectively. The PFT of the human face provides a new signature of the face. The intensity of the PFT is invariant to the shift and out-of-plane rotation within a certain angle. It is also scale invariant within a certain range. We have used Circular Harmonic m-r filtering to achieve the in- plane partial rotation invariance. The recognition decision is based on the intensity and performance of the correlation peak.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Shoude Chang, Marc Rioux, and Chander Prakash Grover "Extracting invariant features of the human face from 3D range data", Proc. SPIE 3905, 28th AIPR Workshop: 3D Visualization for Data Exploration and Decision Making, (5 May 2000); https://doi.org/10.1117/12.384873
PROCEEDINGS
7 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT

2D DOST based local phase pattern for face recognition
Proceedings of SPIE (April 30 2017)
Shape representation by Gabor expansion
Proceedings of SPIE (August 31 1990)
3D face database for human pattern recognition
Proceedings of SPIE (October 12 2008)

Back to Top