Extracting invariant features of the human face from 3D range data

Shoude Chang; Marc Rioux; Chander Prakash Grover

doi:10.1117/12.384873

5 May 2000 Extracting invariant features of the human face from 3D range data

Shoude Chang, Marc Rioux, Chander Prakash Grover

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.

Citation Download Citation

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

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