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Voxel-based 3D occlusion-invariant face recognition using game theory and simulated annealing

机译：基于体素的3D遮挡不变性面部识别使用博弈论和模拟退火

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摘要

A novel voxel-based occlusion-invariant 3D face recognition framework (V3DOFR) based on game theory and simulated annealing is proposed. In V3DOFR approach, 3D meshes are converted to voxel form of sizes 4~3, 8~3, and 16~3. After that, locality preserving projection-based embeddings are computed for removing the sparseness of voxels and generating consistent linear embedding per mesh with size 64 × 3, 128 × 3, and 256 × 3, respectively. The generator of triplets provides the triplets of sizes 64×3×3, 128×3×3, and 256×3×3. The simulated annealing is used to check the threshold value of adversarial triplet loss generated after ensembling losses of different grid sizes. The proposed framework is compared with four well-known methods using three face datasets, namely, Bosphorus, UMBDB, and KinectFaceDB. The performance evaluation has been done using four different cases of experimentations, viz. voxel based face recognition, occlusion invariant face recognition, landmarks based 3D face recognition, and 3D mesh based face recognition. Seven evaluation metrics are used to compare the proposed technique with other methods. The proposed method provides better accuracy and computation time over the other existing techniques in the majority of cases.
机译：提出了一种基于博弈论和模拟退火的基于模拟的基于Voxel的闭塞式无变的3D面识别框架（V3DOFR）。在V3DOFR方法中，3D网格转换为尺寸4〜3,8〜3和16〜3的体素形式。之后，计算基于投影的嵌入的位置，以消除体素的稀疏性，并分别产生具有大小64×3,128×3和256×3的一致线性嵌入。三元组的发电机提供尺寸64×3×3,128×3×3和256×3×3的三胞胎。模拟退火用于检查在合并不同网格尺寸的损耗后产生的对抗性三联损耗的阈值。将所提出的框架与使用三个面部数据集，即博斯普鲁斯，UMBDB和KinectFacedB进行比较了四种众所周知的方法。使用四种不同的实验案件，绩效评估进行了绩效评估。基于体素的面部识别，遮挡不变性面部识别，基于地标3D面部识别，以及3D网的面部识别。七个评估度量标准用于将所提出的技术与其他方法进行比较。该方法提供了大多数情况下的其他现有技术的更好的准确性和计算时间。

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