TY - GEN
T1 - Pose robust 3D face recognition using the RBFN feature
AU - Yang, Ukil
AU - Sohn, Kwanghoon
PY - 2007
Y1 - 2007
N2 - This paper describes a novel global shape (GS) feature of three-dimensional (3D) face data based on the Radial Basis Function Network (RBFN) as well as an extraction method of the proposed feature for 3D face recognition. The features are extracted from facial profiles based on the RBFN. To validate the robustness of the RBFN feature for pose variations, we perform experiments using the test images which consist of five pose variations, and we compare the performance of the proposed feature with those of 3D Principal Component Analysis (3D PCA) and Extended Gaussian Image (EGI). We also perform an experiment about a problem of the holes caused by occlusion region which may appear after the pose compensation of 3D data having one view point. Through these experiments, it is obvious that the RBFN feature outperforms the 3D PCA and the EGI for 3D facial recognition under the pose variable environments.
AB - This paper describes a novel global shape (GS) feature of three-dimensional (3D) face data based on the Radial Basis Function Network (RBFN) as well as an extraction method of the proposed feature for 3D face recognition. The features are extracted from facial profiles based on the RBFN. To validate the robustness of the RBFN feature for pose variations, we perform experiments using the test images which consist of five pose variations, and we compare the performance of the proposed feature with those of 3D Principal Component Analysis (3D PCA) and Extended Gaussian Image (EGI). We also perform an experiment about a problem of the holes caused by occlusion region which may appear after the pose compensation of 3D data having one view point. Through these experiments, it is obvious that the RBFN feature outperforms the 3D PCA and the EGI for 3D facial recognition under the pose variable environments.
UR - http://www.scopus.com/inward/record.url?scp=56549084518&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:56549084518
SN - 9780889866911
T3 - Proceedings of the 7th IASTED International Conference on Visualization, Imaging, and Image Processing, VIIP 2007
SP - 235
EP - 240
BT - Proceedings of the 7th IASTED International Conference on Visualization, Imaging, and Image Processing, VIIP 2007
T2 - 7th IASTED International Conference on Visualization, Imaging, and Image Processing, VIIP 2007
Y2 - 29 August 2007 through 31 August 2007
ER -