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 mes...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.展开更多
基于影像的三维重建技术是获取地形数据的新方法,其重建效果很大程度上取决于影像的特征检测方式。为探究不同特征检测方法对地形三维重建的影响,本文选取SIFT(Scale-Invariant Feature Transform)和ORB(Oriented FAST and Rotated BRI...基于影像的三维重建技术是获取地形数据的新方法,其重建效果很大程度上取决于影像的特征检测方式。为探究不同特征检测方法对地形三维重建的影响,本文选取SIFT(Scale-Invariant Feature Transform)和ORB(Oriented FAST and Rotated BRIEF)特征检测算法,采用增量式运动恢复结构(Structure from Motion,SfM)、多视角密集匹配(Clustering Views for/Patch-based Multi-view Stereo,CMVS/PMVS)、自然邻域插值等方法生成区域地形点云和数字表面模型(Digital Surface Model,DSM),比较分析二者在地形重建中的适用性,结果表明:SIFT生成点云分布均匀,DSM的MRE为2.56%,RMSE为6.36 m,适用于全局的地形三维重建;ORB重建速度快,生成DSM的MRE为2.35%,RMSE为5.17 m,适用于快速的地形三维重建。展开更多
基金Project(XDA06020300)supported by the"Strategic Priority Research Program"of the Chinese Academy of SciencesProject(12511501700)supported by the Research on the Key Technology of Internet of Things for Urban Community Safety Based on Video Sensor networks
文摘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.
