Goodfellow, I.J., Pouget-Abadie, J., Mirza, M, et al. (2014) Generative Adversarial Nets. Proceedings of the 27th International Conference on Neural Information Processing Systems, Volume 2, 2672-2680.
https://dl.acm.org/doi/10.5555/2969033.2969125

has been cited by the following article:

• TITLE: Video-Based Face Recognition with New Classifiers

  AUTHORS: Soniya Singhal, Madasu Hanmandlu, Shantaram Vasikarla

  KEYWORDS: Face Recognition on Videos, Information Sets, Constrained Hanman Transform Classifier, Weighted Hanman Transform Classifier, Video Face Dataset, MobileNet, Vgg-16, Inception Net, ResNet

  JOURNAL NAME: Journal of Modern Physics, Vol.12 No.3, February 26, 2021

  ABSTRACT: An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and also the information set features. Our objective is to cash in on a plethora of deep learning architectures and information set features. The deep learning architectures dig in features from several layers of convolution and max-pooling layers though a placement of these layers is architecture dependent. On the other hand, the information set features depend on the entropy function for the generation of features. A comparative study of deep learning and information set features is made using the well-known classifiers in addition to developing Constrained Hanman Transform (CHT) and Weighted Hanman Transform (WHT) classifiers. It is demonstrated that information set features and deep learning features have comparable performance. However, sigmoid-based information set features using the new classifiers are found to outperform MobileNet features.