International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN. 

Utilizing VGG-19 for Enhanced Face Recognition: A Comprehensive Analysis

    , ,

Author Affiliations

  • 1Department of Computer Science, Dr. K. N. Modi University, Tonk- 304022, Rajasthan, India
  • 2Department of Computer Science, Dr. K. N. Modi University, Tonk- 304022, Rajasthan, India
  • 3Department of Computer Science, Dr. K. N. Modi University, Tonk- 304022, Rajasthan, India

Res. J. Recent Sci., Volume 14, Issue (2), Pages 29-31, April,2 (2025)

Abstract

Face recognition technology has significantly advanced with the development of deep learning models, particularly the VGG-19 architecture, which has become a foundational tool in this field. This paper presents an in-depth analysis of VGG-19, focusing on its architecture, role in face recognition, and integration with other techniques to improve accuracy. The study employs two prominent datasets, Labeled Faces in the Wild (LFW) and CelebA, to evaluate the model's performance. Key steps include data preprocessing, transfer learning, and fine-tuning of the VGG-19 model, followed by an assessment using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The experimental results demonstrate the model's robustness and efficiency, with high accuracy and minimal misclassifications across varying conditions. Challenges encountered, such as high computational requirements and handling occlusions, are addressed through techniques like model pruning and data augmentation. Future work will explore hybrid models and optimization for real-time applications.

References

  1. Sun, Y., Chen, Y., Wang, X., & Tang, X. (2014)., Deep learning face representation by joint identification-verification., Advances in neural information processing systems, 27.
  2. Wang, H., & Guo, L. (2021)., Research on face recognition based on deep learning., In 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) (pp. 540-546). IEEE.
  3. Perdana, A. B., & Prahara, A. (2019)., Face recognition using light-convolutional neural networks based on modified vgg16 model., In 2019 International Conference of Computer Science and Information Technology (ICoSNIKOM) (pp. 1-4). IEEE.
  4. Prakash, R. M., Thenmoezhi, N., & Gayathri, M. (2019)., Face recognition with convolutional neural network and transfer learning., In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 861-864). IEEE.
  5. Lu, P., Song, B., & Xu, L. (2021)., Human face recognition based on convolutional neural network and augmented dataset., Systems Science & Control Engineering, 9(sup2), 29-37.
  6. Vimal, C., & Shirivastava, N. (2022)., Face and face-mask detection system using vgg-16 architecture based on convolutional neural network., International Journal of Computer Applications, 183(50), 16-21.
  7. Tang, D., & Hao, J. (2022). A deep map transfer learning method for face recognition in an unrestricted smart city environment. Sustainable Energy Technologies and Assessments, 52, 102207., undefined, undefined
  8. Xiong, F., & You, Z. (2023)., Research on face image recognition system based on computer artificial intelligence technology., In 2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI) (pp. 1158-1161). IEEE.
  9. Bewoor, M., Patil, S., Kushwaha, S., Tandon, S., Trivedi, S., & Pawar, A. (2023)., Face recognition using open CV and VGG 16 transfer learning., In AIP Conference Proceedings (Vol. 2890, No. 1). AIP Publishing.
  10. Sri Hari, D., Rao, T. C. S., Venkataramana, T., & Himabindu , D. (2023)., Face Recognition Using Computer Vision and CNN Algorithm., International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 252–255.