Volume Deep Face: A 3D Face Descriptor for Face Authentication System
Article Sidebar
Main Article Content
Abstract
In this paper, we introduce the Volume Deep Face (VDF), a novel face representation proposed for the face authentication system. VDF provides a fast and compact representation of faces using deep learning, enabling one to encode more distinctive features. Using our proposed method, images can be generated to form a 3D VDF representation or a 2D face descriptor (2DFD). The 3D VDF is created from multiple images in the training set, while the 2DFD is generated from a single image during the testing phase. The matching confidence is evaluated using our new volume matching. Our face authentication system is verified with extensive experiments on the XM2VTS database.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Journal of TCI is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence, unless otherwise stated. Please read our Policies page for more information...
References
D. Ying., “Exploring PCA-based feature representations of image pixels via CNN to enhance food image
segmentation,” 10.48550/arXiv.2411.01469., 2024.
P. N. Belhumeur, J. P. Hespanha and D. J. Kriegman, “Eigenfaces vs. Fisherfaces: recognition using class
specific linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.
T. Ahonen, A. Hadid and M. Pietikainen, “Face Description with Local Binary Patterns: Application to Face Recognition,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, Dec. 2006.
Y. Taigman, M. Yang, M. Ranzato, L. Wolf, “Deep-Face: Closing the Gap to Human-Level Performance in Face Verification,” Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1701-1708, 2014.
K. Messer, J. Kittler, M. Sadeghi, S. Marcel, C. Marcel, S. Bengio, F. Cardinaux, C. Sandersonan,
J. Czyz, L. Vandendorpe, S. Srisuk, M. Petrou, W. Kurutach, A. Kadyrov, R. Paredes, B. Kepenekci, F. B. Tek, G. B. Akar, F. Deravi and N. Mavity, “Face Verification Competition on the XM2VTS database,” in Proc. of 4th Int. Conf. Audio and Video Based Biometric Person Authentication, Lecture Notes in Computer Science, Vol. 2688, Springer-Verlag, pp. 964-974, 2003.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell, “Caffe: Convolutional Architecture for Fast Feature Embedding,” arXiv preprint arXiv:1408.5093, 2014.
S. Srisuk, et.al., “Robust Face Recognition based on weighted DeepFace,” 2017 International Electrical Engineering Congress (iEECON), Pattaya, Thailand, 2017, pp. 1-4, doi: 10.1109/IEECON.2017.8075885.
C. Li, et. al., “YOLOv6: A Single-Stage Object DetectionFramework for Industrial Applications,” Technical
Reports, 2022, https://arxiv.org/abs/2209.02976.
J. Yangqing, et. al, “Caffe: Convolutional Architecture for Fast Feature Embedding,” arXiv preprint
arXiv:1408.5093.
Y. LeCun, et. al, “Handwritten Digit Recognition with a Back-Propagation Network,” Advances in Neural
Information Processing Systems, vol. 2, 1989.
I. Goodfellow, Y. Bengio and A. Courville, “Deep Learning,” MIT Press, 2016.
N. Buduma, N. Buduma and J. Papa, “Fundamentals of Deep Learning, “O’Reilly Media, Inc., 2022.
F. Schroff, D. Kalenichenko and J. Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering,”
Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 815–823, 2015.
J. Deng, J. Guo, N. Xue and S. Zafeiriou, “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,”
Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 4690–4699, 2019.
O. M. Parkhi, A. Vedaldi and A. Zisserman, “Deep Face Recognition,” Proc. British Machine Vision Conf. (BMVC), 2015.
I. Kemelmacher-Shlizerman and S. M. Seitz, “Face Reconstruction in the Wild,” Proc. IEEE Int. Conf.Computer Vision (ICCV), pp. 1746–1753, 2011.
K. He, X. Zhang, S. Ren and J. Sun, “Deep Residual Learning for Image Recognition,” Proc. IEEE Conf.
Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016.
A. Krizhevsky, I. Sutskever and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural
Networks,” Adv. Neural Information Processing Systems (NeurIPS), pp. 1097–1105, 2012.
J. Redmon, S. Divvala, R. Girshick and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” Proc. IEEE Conf. Computer Vision andPattern Recognition (CVPR), pp. 779–788, 2016.
H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou and Z. Li, “CosFace: Large Margin Cosine
Loss for Deep Face Recognition,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 5265–5274, 2018.
Q. Cao, L. Shen, W. Xie, O. M. Parkhi and A. Zisserman, “VGGFace2: A Dataset for Recognising Faces
across Pose and Age,” Proc. IEEE International Conference on Automatic Face and Gesture Recognition
(FG), pp. 67–74, 2018.
Y. Guo, L. Zhang, Y. Hu, X. He and J. Gao, “MSCeleb-1M: A Dataset and Benchmark for Large-Scale
Face Recognition,” Proc. European Conf. Computer Vision (ECCV), pp. 87–102, 2016.
J. Deng, J. Guo, Y. Zhou, J. Yu, I. Kotsia and S. Zafeiriou, “RetinaFace: Single-stage Dense Face
Localisation in the Wild,” in Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition
(CVPR), pp. 5202–5211, 2020.
K. Zhang, Z. Zhang, Z. Li and Y. Qiao, “Joint Face Detection and Alignment Using Multitask Cascaded
Convolutional Networks,” in IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1499–1503, 2016.
