Face recognition using SIFT features under 3D meshes

Abstract

Expression, occlusion, and pose variations are three main challenges for 3D face recognition. A novel method is presented to address 3D face recognition using scale-invariant feature transform (SIFT) features on 3D meshes. After preprocessing, shape index extrema on the 3D facial surface are selected as keypoints in the difference scale space and the unstable keypoints are removed after two screening steps. Then, a local coordinate system for each keypoint is established by principal component analysis (PCA). Next, two local geometric features are extracted around each keypoint through the local coordinate system. Additionally, the features are augmented by the symmetrization according to the approximate left-right symmetry in human face. The proposed method is evaluated on the Bosphorus, BU-3DFE, and Gavab databases, respectively. Good results are achieved on these three datasets. As a result, the proposed method proves robust to facial expression variations, partial external occlusions and large pose changes.

3D Human Facial Traits’ Analysis for Ethnicity Recognition Using Deep Learning

“…The authors posed side scans from the UND dataset and frontal faces from FRGC v2.0 to examine the SIFT robustness against the pose variation and detect the identical faces between these two datasets that provided multiple pose scans for each individual. Zhang et al [22] used the shape index extrema as interesting points on the face surface. The author proposed a scale change procedure to eliminate a set of unstable key points initially, then extracted the features.…”