Abstract In this paper, an efficient multi-scale hybrid approach is proposed to tackle two main problems in three-dimensional (3D) face recognition, namely the singularity of scale features representation and underexplored locality in dictionary learning. The multi-scale features space representation is developed based on the new 3D faces generated by the Gaussian filter. The locality-sensitive Riemannian sparse representation-based classifier is also constructed to accurately recognize faces with various expressions, poses and occlusions. Two sets of face recognition experiment, one that includes expression variations, and the another that includes pose and occlusion variations, are conducted to compare the performance of the proposed approach against other benchmark 3D face recognition algorithms. The recognition accuracies of the proposed algorithm to both Neutral vs. Neutral achieved on Face Recognition Grand Challenge (FRGC) v2.0 database and Bosphorus database are 100%.

中文翻译：

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基于稀疏表示的分类器和局部协方差描述符融合的多尺度三维人脸识别方法

摘要 在本文中，提出了一种有效的多尺度混合方法来解决三维（3D）人脸识别中的两个主要问题，即尺度特征表示的奇异性和字典学习中未充分探索的局部性。多尺度特征空间表示是基于高斯滤波器生成的新 3D 人脸开发的。还构建了基于局部敏感黎曼稀疏表示的分类器，以准确识别具有各种表情、姿势和遮挡的人脸。进行了两组人脸识别实验，一组包括表情变化，另一组包括姿势和遮挡变化，以将所提出的方法与其他基准 3D 人脸识别算法的性能进行比较。