Paper Info
Title
Voxel-based 3D occlusion-invariant face recognition using game theory and simulated annealing
Abstract
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 x 3, 128 x 3, and 256 x 3, respectively. The generator of triplets provides the triplets of sizes 64x3x3, 128x3x3, and 256x3x3. 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.
Year
DOI
Venue
2020
10.1007/s11042-020-09331-5
MULTIMEDIA TOOLS AND APPLICATIONS
Keywords
DocType
Volume
3D mesh,Voxelization,Adversarial triplet loss,Generator,Discriminator,Simulated annealing
Journal
79.0
Issue
ISSN
Citations 
35-36
1380-7501
1
PageRank 
References 
Authors
0.36
0
2
Name
Order
Citations
PageRank
Sahil Sharma1358.65
Vijay Kumar222921.59