Authors : Shraddha Mishra; Manvi Chahar; Shivani Jaswal

Volume/Issue : Volume 9 - 2024, Issue 8 - August

Google Scholar : https://tinyurl.com/7jen6cky

Scribd : https://tinyurl.com/3fjzjsua

DOI : https://doi.org/10.38124/ijisrt/IJISRT24AUG265

Abstract : Advancements in face synthesis technology have enabled innovative methods for modeling facial aging. This research paper focuses primarily on creating a robust face aging model using deep learning and Generative Adversarial Networks (GANs), trained on a diverse dataset of facial images. The proposed approach captures both global features and local textures to produce realistic age-progressed images while preserving the subject's identity. This paper also examines face synthesis techniques, with specific emphasis for the various practical usage of GANs. The key objective of our project is to upgrade both the discriminator and the generator parts of GANs to generate more realistic, age- progressed face images. We evaluated the model using quantitative metrics and qualitative assessments, demonstrating its effectiveness. Additionally, we address ethical considerations, proposing guidelines for responsible use. Our study offers a novel framework with significant applications in security, forensics, and entertainment, and suggests future research directions to improve accuracy and ethical standards.

Keywords : Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs), Generator, Discriminator, IP-CGAN.

References :

1. Pranoto, H., Heryadi, Y., Warnars, H. L. H. S., & Budiharto, W. (2022). Recent generative adversarial approach in face aging and dataset review. IEEE Access, 10, 28693-28716.
2. Antipov, G., Baccouche, M., & Dugelay, J. L. (2017, September). Face aging with conditional generative adversarial networks. In 2017 IEEE international conference on image processing (ICIP) (pp. 2089-2093). IEEE.
3. Sharma, Neha, Reecha Sharma, and Neeru Jindal. "Prediction of Face Age Progression with Generative Adversarial Networks." Multimedia Tools and Applications 80.25 (2021): 33911-33935.
4. Kaneko, Takuhiro. "Generative Adversarial Networks: Foundations and Applications." Acoustical Science and Technology 39.3 (2018): 189-197.
5. Y. Singh, M. Saini and Savita, "Impact and Performance Analysis of Various Activation Functions for Classification Problems," 2023 IEEE International Conference on Contemporary Computing and Communications (InC4), Bangalore, India, 2023, pp. 1-7, doi: 10.1109/InC457730.2023.10263129.
6. Sharma, Neha, Reecha Sharma, and Neeru Jindal. "An Improved Technique for Face Age Progression and Enhanced Superresolution with Generative Adversarial Networks." Wireless Personal Communications 114 (2020): 2215-2233.
7. Z. Zhang, Y. Song, and H. Qi, “Age Progression/Regression By Conditional Adversarial Autoencoder,” In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jul. 2017, Pp. 4352–4360.
8. X. Tang, Z. Wang, W. Luo, and S. Gao, ‘‘Face Aging with Identity-Preserved Conditional Generative Adversarial Networks,’’ In Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., Jun. 2018, Vol. 9, No. 1, Pp. 7939–7947, Doi: 10.1109/CVPR.2018.00828.
9. P. D. S. Prasad, R. Tiwari, M. L. Saini and Savita, "Digital Image Enhancement using Conventional Neural Network," 2023 2nd International Conference for Innovation in Technology (INOCON), Bangalore, India, 2023, pp. 1-5, doi: 10.1109/INOCON57975.2023.10100995.
10. M. Sohail, M. Lal Saini, V. P. Singh, S. Dhir and V. Patel, "A Comparative Study of Machine Learning and Deep Learning Algorithm for Handwritten Digit Recognition," 2023 6th International Conference on Contemporary Computing and Informatics (IC3I), Gautam Buddha Nagar, India, 2023, pp. 1283-1288, doi: 10.1109/IC3I59117.2023.10397956
11. Sarmah, J., Saini, M.L., Kumar, A., Chasta, V. (2024). Performance Analysis of Deep CNN, YOLO, and LeNet for Handwritten Digit Classification. In: Sharma, H., Chakravorty, A., Hussain, S., Kumari, R. (eds) Artificial Intelligence: Theory and Applications. AITA 2023. Lecture Notes in Networks and Systems, vol 844. Springer, Singapore. https://doi.org/10.1007/978-981-99-8479-4_16
12. M. L. Saini, B. Tripathi, J. Kaushal and A. Garg, "A Hybrid Model for Diagnosis Cardiovascular Disease Using Clinical Features, ECG and MRI," 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), Ballari, India, 2023, pp. 1-6, doi: 10.1109/AIKIIE60097.2023.10390299.
13. M. Lal Saini, B. Tripathi and M. S. Mirza, "Evaluating the Performance of Deep Learning Models in Handwritten Digit Recognition," 2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS), Tashkent, Uzbekistan, 2023, pp. 116-121, doi: 10.1109/ICTACS59847.2023.10390027.
14. Chopra and M. Lal Saini, "Comparison Study of Different Neural Network Models for Assessing Employability Skills of IT Graduates," 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA), Theni, India, 2023, pp. 189-194, doi: 10.1109/ICSCNA58489.2023.10368605.
15. E. G. Kumar, M. Lal Saini, S. A. Khadar Ali and B. B. Teja, "A Clinical Support System for Prediction of Heart Disease using Ensemble Learning Techniques," 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA), Theni, India, 2023, pp. 926-931, doi: 10.1109/ICSCNA58489.2023.10370569.
16. S. P. Kumar Mygapula, M. Lal Saini and C. S. Raj Dheeraj, "Performance Evaluation of Machine Learning Algorithms for Prediction of Cardiac Failure," 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA), Theni, India, 2023, pp. 1599-1604, doi: 10.1109/ICSCNA58489.2023.10368606.
17. S. Chalechema, M. L. Saini, I. Perla and A. V. Shivanand, "Customer Segmentation Using K Means Algorithm and RFM Model," 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, 2023, pp. 393-398, doi: 10.1109/ICCCIS60361.2023.10425556.
18. S. Mittal, R. Agarwal, M. L. Saini and A. Kumar, "A Logistic Regression Approach for Detecting Phishing Websites," 2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT), Faridabad, India, 2023, pp. 76-81, doi: 10.1109/ICAICCIT60255.2023.10466221.
19. V. Prabhas, M. Lal Saini, C. Mohith, R. Kumar and B. Tripathi, "Segmentation of E-Commerce Data Using K-Means Clustering Algorithm," 2023 Global Conference on Information Technologies and Communications (GCITC), Bangalore, India, 2023, pp. 1-6, doi: 10.1109/GCITC60406.2023.10426132.
20. M. L. Saini, A. Patnaik, Mahadev, D. C. Sati and R. Kumar, "Deepfake Detection System Using Deep Neural Networks," 2024 2nd International Conference on Computer, Communication and Control (IC4), Indore, India, 2024, pp. 1-5, doi: 10.1109/IC457434.2024.10486659.