A Hybrid Regression-Based Network Model for Continuous Face Recognition and Authentication

This research proposes a continuous remote biometric user authentication system implemented with a face recognition model pre-trained on face images. This work develops an algorithm combining the Hybrid Block Overlapping KT Polynomials (HBKT) and Regression-based Support Vector Machine (RSVM) methods for a face recognition-based remote user authentication system that uses a model pre-trained on the ORL, Face94 and GT datasets to recognize authorized users from face images captured through a webcam for continuous remote biometric user authentication. HBKT polynomials enhance feature extraction by capturing local and global facial patterns, while RSVM improves classification performance through efficient regression-based decision boundaries. The system can continuously capture user face images from the user’s webcam for user authentication, but it can be affected by lighting variations, occlusion, and computational overhead from continuous image capture. This has been implemented in a Python program. The proposed method, when compared to previous state-of-the-art algorithms, was observed to have higher F-measure, accuracy, and speed, for most of the cases. The proposed method was observed to have accuracies of 98.82% (ORL dataset), 96.73% (GT dataset), and 95.9% (Face94 dataset).

@article{Devisetty2024,
  title     = {A Hybrid Regression-Based Network Model for Continuous Face Recognition and Authentication},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Bhanu Kiran Devisetty and Ayush Goyal and Avdesh Mishra and Mais W Nijim and David Hicks and George Toscano},
  doi       = {10.14569/IJACSA.2024.0151005},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151005}
}