UR3D-C: Linear dimensionality reduction for efficient 3D face recognition

Omar Ocegueda, Georgios Passalis, Theoharis Theoharis, Shishir K. Shah, Ioannis A. Kakadiaris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations

Abstract

We present a novel approach for computing a compact and highly discriminant biometric signature for 3D face recognition using linear dimensionality reduction techniques. Initially, a geometry-image representation is used to effectively resample the raw 3D data. Subsequently, a wavelet transform is applied and a biometric signature composed of 7,200 wavelet coefficients is extracted. Finally, we apply a second linear dimensionality reduction step to the wavelet coefficients using Linear Discriminant Analysis and compute a compact biometric signature. Although this biometric signature consists of just 57 coefficients, it is highly discriminant. Our approach, UR3D-C, is experimentally validated using four publicly available databases (FRGC v1, FRGC v2, Bosphorus and BU-3DFE). State-of-the-art performance is reported in all of the above databases.

Original languageEnglish (US)
Title of host publication2011 International Joint Conference on Biometrics, IJCB 2011
DOIs
StatePublished - 2011
Event2011 International Joint Conference on Biometrics, IJCB 2011 - Washington, DC, United States
Duration: Oct 11 2011Oct 13 2011

Publication series

Name2011 International Joint Conference on Biometrics, IJCB 2011

Conference

Conference2011 International Joint Conference on Biometrics, IJCB 2011
Country/TerritoryUnited States
CityWashington, DC
Period10/11/1110/13/11

ASJC Scopus subject areas

  • Biotechnology

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