International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Deep Learning based Human Recognition using Integration of GAN and Spatial Domain Techniques

  • Sharath, S (Department of Electronics and Communication Engineering, Government Engineering College, K R Pete, Affiliated to Visvesvaraya Technological University) ;
  • Rangaraju, HG (Department of Electronics and Communication Engineering, Government SKSJ Technological Institute, Bangalore, Affiliated to Visvesvaraya Technological University)
  • Received : 2021.08.05
  • Published : 2021.08.30
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Abstract

Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.

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References

  1. 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).
  2. Y. LeCun, Y. Bengio, and G. Hinton.: Deep learning. Nature, vol. 521, no. 7553, pp. 436-444, (2015). https://doi.org/10.1038/nature14539
  3. 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).
  4. 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).
  5. 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).
  6. A. Brock, J. Donahue, and K. Simonyan.: Large Scale GAN Training for High Fidelity Natural Image Synthesis. (2018), http://arxiv.org/abs/1809.11096.
  7. 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).
  8. 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
  9. 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).
  10. 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).
  11. 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).
  12. 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).
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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).
  18. V B Raj and K Hareesh, "Review on Generative Adversarial Networks.: IEEE International Conference on Communication and Signal Processing (ICCSP), pp. 0479-0482, (2020).
  19. 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).
  20. 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).
  21. 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).
  22. 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).
  23. R Yin.: Multi-Resolution Generative Adversarial Networks for Tiny-Scale Pedestrian Detection. IEEE International Conference on Image Processing, pp. 1665-1669, (2019).
  24. 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).
  25. 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).
  26. 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).
  27. 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).
  28. 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).
  29. 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).
  30. 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).
  31. Z. Zhai and J. Zhai.: Identity-Preserving Conditional Generative Adversarial Network. IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1-5, (2018).
  32. 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).
  33. 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).
  34. https://www.kaggle.com/greg115/celebrities100k?select=100k.txt
  35. Micheal J Lyons.: The Japanese Female Face Expression (JAFFE) Database. (1998), http://www.karsl.org/jaffe.html.
  36. http://www.cl.cam.ac.uk/research/dtg/attractive/face database. htm. AT&T Laboratories Cambridge.: The ORL Database of Faces. (1994).
  37. http://cvc.cs.yale.edu/cvc/projects/yalefaces/yalefaces.html. Yale University.: The Yale Face Database. (1997)
  38. 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
  39. 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).
  40. 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
  41. 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)
  42. Ying Wen.: A Novel Dictionary based SRC for Face Recognition. IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2582-2586, (2017).
  43. 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).
  44. 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.
  45. 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
  46. 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).
  47. 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).
  48. 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
  49. 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).