Abstract
With big data growing rapidly in importance over the past few years, Virtual Reality (VR) display technology has gained widespread attention. Especially, virtual avatars with unique identification are becoming increasingly important. Traditional generation methods are complicated, time consuming and low image quality. To improve the quality and rendering speed of generated virtual avatars, we present a set of optimized parameters of GAN based on deep learning. The experimental results showed that the best image quality is achieved in the case of ADAM (Optimization Function), BCE (Loss Function), l-r = 0.00075 (Learning Rate), the epoch = 200. The questionnaire survey showed that the recognition accuracy could be up to 84.29%. The conclusion of the questionnaire survey is consistent with our experimental results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Goodfellow, I., et al.: Generative adversarial nets. Adv. Neural Inform. Process. Syst. 27 (2014)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Zhang, H., et al.: Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp. 5907–5915 (2017)
Isola, P., Zhu, J. Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1125–1134 (2017)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp. 2223–2232 (2017)
Mahalingaiah, K., Matichuk, B.: ProDeblurGAN: progressive growing of GANs for blind motion deblurring in face recognition. In: McDaniel, T., Berretti, S., Curcio, I.D.D., Basu, A. (eds.) Smart Multimedia: Second International Conference, ICSM 2019, San Diego, CA, USA, December 16–18, 2019, Revised Selected Papers, pp. 439–450. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-54407-2_37
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110–8119 (2020)
Zhou, W., Anastasio, M.A.: Markov-chain monte carlo approximation of the ideal observer using generative adversarial networks. In: Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment, vol. 11316, p. 113160D. International Society for Optics and Photonics (2020)
Xian, Y., Sharma, S., Schiele, B., Akata, Z.: f-vaegan-d2: A feature generating framework for any-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10275–10284 (2019)
Khan, M., Chakraborty, S., Astya, R., Khepra, S.: Face detection and recognition using openCV. In: 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp. 116–119. IEEE (2019)
Koranne, S.: Hierarchical data format 5: HDF5. In: Koranne, S. (ed.) Handbook of open source tools, pp. 191–200. Springer US, Boston, MA (2011). https://doi.org/10.1007/978-1-4419-7719-9_10
Zhai, D., Liu, X., Chang, H., Zhen, Y., Chen, X., Guo, M., Gao, W.: Parametric local ultiview hamming distance metric learning. Pattern Recognition 75, 250–262 (2018)
Li, B., Ming, D., Yan, W., Sun, X., Tian, T., Tian, J.: Image matching based on two-column histogram hashing and improved RANSAC. IEEE Geosci. Remote Sens. Lett. 11(8), 1433–1437 (2014)
Dubey, A.K., Jain, V.: Comparative study of convolution neural network’s relu and leaky-relu activation functions. In: Mishra, S., Sood, Y.R., Tomar, A. (eds.) Applications of Computing, Automation and Wireless Systems in Electrical Engineering: Proceedings of MARC 2018, pp. 873–880. Springer Singapore, Singapore (2019). https://doi.org/10.1007/978-981-13-6772-4_76
Pratiwi, H., et al.: Sigmoid activation function in selecting the best model of artificial neural networks. J. Phys.: Conf. Ser. 1471(1), 012010 (2020)
Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)
Kurbiel, T., Khaleghian, S.: Training of deep neural networks based on distance measures using RMSProp. arXiv preprint arXiv:1708.01911 (2017)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Acknowledgements
The authors gratefully acknowledge the participants in the user study and the anonymous reviewers for their constructive comments.
Funding
Funding by Basic Science (Natural Science) research project of Jiangsu Province(21KJB520038), the Scientific Research Foundation for Advanced Talents, Nanjing Institute of Technology (YKJ201979), and Natural Science Foundation of Jiangsu Province, China (Grant No. BK20201468).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhao, J., Peng, M., Zhu, BL., Li, LL. (2023). Study on Random Generation of Virtual Avatars Based on Big Data. In: Tian, Y., Ma, T., Jiang, Q., Liu, Q., Khan, M.K. (eds) Big Data and Security. ICBDS 2022. Communications in Computer and Information Science, vol 1796. Springer, Singapore. https://doi.org/10.1007/978-981-99-3300-6_26
Download citation
DOI: https://doi.org/10.1007/978-981-99-3300-6_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-3299-3
Online ISBN: 978-981-99-3300-6
eBook Packages: Computer ScienceComputer Science (R0)