References
- I. Goodfellow, J Pouget-Abadie, Mehdi Mirza, Bingu, David WardeFarley, Sherjil Ozair, Aaron Courville, Yoshua Bengio.: Generative Adversarial Nets. Proc. of NIPS, pp.2672-2680, (2015).
- Y. LeCun, Y. Bengio, and G. Hinton.: Deep learning. Nature, vol. 521, no. 7553, pp. 436-444, (2015). https://doi.org/10.1038/nature14539
- Y. Shen, P. Luo, P. Luo, J. Yan, X. Wang and X. Tang.: Face ID-GAN: Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis. IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 821-830, (2018).
- Huikai Wu, Shuai Zheng, Junge Zhang and Kaiqi Huang.: GP-GAN: Towards Realistic High-Resolution Image Blending. ACM International Conference on Multimedia, pp 2487-2495, (2019).
- T. Karras, S. Laine and T. Aila.: A Style-Based Generator Architecture for Generative Adversarial Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, (2020).
- A. Brock, J. Donahue, and K. Simonyan.: Large Scale GAN Training for High Fidelity Natural Image Synthesis. (2018), http://arxiv.org/abs/1809.11096.
- X. Tang, Z. Wang, W. Luo and S. Gao.: Face Aging with Identity-Preserved Conditional Generative Adversarial Networks. IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7939-7947, (2018).
- Z. Huang, S. Chen, J. Zhang and H. Shan.: PFA-GAN: Progressive Face Aging with Generative Adversarial Network. IEEE Transactions on Information Forensics and Security, vol. 16, pp. 2031-2045, (2021). https://doi.org/10.1109/TIFS.2020.3047753
- T. Ojala, M. Pietikainen and D. Harwood.: Performance Evaluation of Texture Measures with Classification based on Kullback Discrimination of Distributions. IEEE International Conference on Pattern Recognition, vol.1, pp. 582-585, (1994).
- Sufang Zhang, Qinghai Miao, Min huang, Xiangyu Zhu,Yingying Chen, Zhen Lei, and Jinqiao Wang.: Pose-Weighted Gan for Photorealistic Face Frontalization. IEEE International Conference on Image Processing (ICIP), pp. 2384-2388, (2019).
- Z. Zhang, H. Zhang, H. Liu, S. Xin, N. Xiao and L. Zhang.: Frontal Face Generation based Multi-Angle Face Identification System. IEEE International Conference on Computer, Control and Robotics (ICCCR), pp. 329-334, (2021).
- Y. Kawai, M. Seo and Y. Chen.: Automatic Generation of Facial Expression using Generative Adversarial Nets. IEEE Global Conference on Consumer Electronics (GCCE), pp. 278-280, (2018).
- Y. He and S. Chen.: Person-Independent Facial Expression Recognition Based on Improved Local Binary Pattern and Higher-Order Singular Value Decomposition. IEEE Access, vol. 8, pp. 190184-190193, (2020). https://doi.org/10.1109/ACCESS.2020.3032406
- N. Alpaslan and K. Hanbay.: Multi-Resolution Intrinsic Texture Geometry-Based Local Binary Pattern for Texture Classification. IEEE Access, vol. 8, pp. 54415-54430, (2020). https://doi.org/10.1109/ACCESS.2020.2981720
- X. Luan, H. Geng, L. Liu, W. Li, Y. Zhao and M. Ren.: Geometry Structure Preserving Based GAN for Multi-Pose Face Frontalization and Recognition. IEEE Access, vol. 8, pp. 104676-104687, (2020). https://doi.org/10.1109/ACCESS.2020.2996637
- M. Liu, J. Liu, P. Zhang and Q. Li.: PA-GAN: A Patch-Attention Based Aggregation Network for Face Recognition in Surveillance." IEEE Access, vol. 8, pp. 152780-152789, (2020). https://doi.org/10.1109/ACCESS.2020.3017779
- Y. Yin, S. Jiang, J. P. Robinson and Y. Fu.: Dual-Attention GAN for Large-Pose Face Frontalization. IEEE International Conference on Automatic Face and Gesture Recognition, pp. 249-256, (2020).
- V B Raj and K Hareesh, "Review on Generative Adversarial Networks.: IEEE International Conference on Communication and Signal Processing (ICCSP), pp. 0479-0482, (2020).
- S S Ghosh, Y Hua, S S Mukherjee and N M Robertson.: Improving Detection and Recognition of Degraded Faces by Discriminative Feature Restoration Using GAN. IEEE International Conference on Image Processing (ICIP), pp. 2146-2150, (2020).
- T Mukhiddin, W Lee, S Lee and T Rashid.: Research Issues on Generative Adversarial Networks and Applications. IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 487-488, (2020).
- Prabhat, Nishant and D Kumar Vishwakarma.: Comparative Analysis of Deep Convolutional Generative Adversarial Network and Conditional Generative Adversarial Network using Hand Written Digits. IEEE International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1072-1075, (2020).
- Zhe Li, Qinghua Tian, Qi Zhang, Kuo Wang,Feng Tian, Chenda Lu, Leijing Yang, and Xiangjun Xin.: An improvement on the CNN-based OAM Demodulator via Conditional Generative Adversarial Networks. IEEE International Conference on Optical Communications and Networks (ICOCN), pp. 1-3, (2019).
- R Yin.: Multi-Resolution Generative Adversarial Networks for Tiny-Scale Pedestrian Detection. IEEE International Conference on Image Processing, pp. 1665-1669, (2019).
- G J Hsu, C Tang and M H Yap.: Face Synthesis and Recognition Using Disentangled Representation-Learning Wasserstein GAN. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2371-2379, (2019).
- J. Ma and F. Zhou.: Multi-poses Face Frontalization based on Pose Weighted GAN. IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 1271-1276, (2019).
- S Radhakrishnan and C Jay Kuo.: Synthetic to Real World Image Translation Using Generative Adversarial Networks. IEEE International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1-6, (2018).
- T Zhang, A Wiliem, S Yang and B Lovell.:TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition. IEEE International Conference on Biometrics (ICB), pp. 174-181, (2018).
- H. Yang, Z. Zhang and L. Yin.: Identity-Adaptive Facial Expression Recognition through Expression Regeneration using Conditional Generative Adversarial Networks. IEEE International Conference on Automatic Face & Gesture Recognition, pp. 294-301, (2018).
- K. D. B. Mudavathu, M. V. P. C. S. Rao and K. V. Ramana.: Auxiliary Conditional Generative Adversarial Networks for Image Data Set Augmentation. IEEE International Conference on Inventive Computation Technologies (ICICT), pp. 263-269, (2018).
- Y Shen, P Luo, P Luo, J Yan, X Wang and X Tang.: Face ID-GAN: Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis. IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 821-830, (2018).
- Z. Zhai and J. Zhai.: Identity-Preserving Conditional Generative Adversarial Network. IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1-5, (2018).
- R. Huang, S. Zhang, T. Li and R. He.: Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis. IEEE International Conference on Computer Vision (ICCV), pp. 2458-2467, (2017).
- J. Deng, S. Cheng, N. Xue, Y. Zhou and S. Zafeiriou.: UV-GAN: Adversarial Facial UV Map Completion for Pose-Invariant Face Recognition. IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7093-7102, (2018).
- https://www.kaggle.com/greg115/celebrities100k?select=100k.txt
- Micheal J Lyons.: The Japanese Female Face Expression (JAFFE) Database. (1998), http://www.karsl.org/jaffe.html.
- http://www.cl.cam.ac.uk/research/dtg/attractive/face database. htm. AT&T Laboratories Cambridge.: The ORL Database of Faces. (1994).
- http://cvc.cs.yale.edu/cvc/projects/yalefaces/yalefaces.html. Yale University.: The Yale Face Database. (1997)
- J. Liu, Q Li, P Zhang, G Zhang and M Liu.: Unpaired Domain Transfer for Data Augment in Face Recognition. IEEE Access, vol. 8, pp. 39349-39360, (2020). https://doi.org/10.1109/ACCESS.2020.2976207
- J. Zhu, T. Park, P. Isola and A. A. Efros.: Unpaired Imageto-Image Translation using Cycle-Consistent Adversarial Networks. IEEE International Conference on Computer Vision (ICCV), pp. 2242-2251, (2017).
- Timo Ahonen, Abdenour Hadid, and Matti Pietikainen.: Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine learning, 28(12), pp.2037-2041, (2006). https://doi.org/10.1109/TPAMI.2006.244
- Narasimha Reddy B. V, Chidambaram A, K B Raja and Venugopal. K R.: Face Recognition Based on LBP of GLCM Symmetrical Local Regions. International Journal of Image Processing and Visual Communication, ISSN 2319-1724: 6(1), pp1-17, (2019)
- Ying Wen.: A Novel Dictionary based SRC for Face Recognition. IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2582-2586, (2017).
- Mohannad Abuzneid and Ausif Mahmood, "Face Recognition Framework based on Correlated Images and Back-Propagation Neural Network", IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), pp. 1-7, (2018).
- Jun Fan, Qiaolin Ye and Ning Ye.: Enhanced Adaptive Locality Preserving Projections for Face Recognition.: IAPR Asian Conference on Pattern Recognition (ACPR), pp. 594-598, 2017.
- Jun Kong, Min Chen, Min Jiang, Jinhua Sun and Jian Hou.: Face Recognition Based on CSGF(2D)2PCANet.: IEEE Access, Vol.6, pp. 45153-45165, (2018). https://doi.org/10.1109/ACCESS.2018.2865425
- P. Rangsee, K. B. Raja and K. R. Venugopal.: Nibble-Based Face Recognition Using Convolution of Hybrid Features.: IEEE International Conference on Imaging, Signal Processing and Communication (ICISPC), pp. 112-116, (2019).
- Santosh Kumar Jami, Srinivasa Rao Chalamala and Krishna Rao Kakkirala.: Cross Local Gabor Binary Pattern Descriptor with Probabilistic Linear Discriminant Analysis for Pose-Invariant Face Recognition.: UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim), pp. 39-44, (2017).
- Guangyi Chen, Tien D. Bui and Adam Krzyzak.: Filter-based face recognition under varying illumination.: IET Biometrics, 7 (6), pp. 628-635, (2018). https://doi.org/10.1049/iet-bmt.2016.0195
- Swarup Kumar Dandpat, Sukadev Meher and Vivek Bopche.: Uneven Illumination Compensation for Unconstrained Face Recognition Using LBP, IEEE International Conference for Convergence in Technology (I2CT), pp. 1-6, (2018).