Convolutional Neural Networks ( CNNs) have been developed as powerful models for image recognition problems requiring large-scale labeled training data. However, estimating millions parameters of deep CNNs requires a huge amount of labeled samples, restricting CNNs being applied to problems with limited training data. To address this problem, a two-phase method combining data augmentation and CNN transfer learning i.e., fine-tuning pre-trained CNN models are studied herein. In this paper, we focus on the case of a single sample face recognition problem, intending to develop a real-time visual-based presence application. In this context, five well-known pre-trained CNNs were evaluated. The experimental results prove that DenseNet121 is the best model for dealing with practice problems (up to 99% top-1 accuracy) is the best and most robust model for dealing with the single sample per person problem, which are related to using deep CNNs on a small dataset and specifically to single sample per person face recognition task.


F. Filippidou and G. A. Papakostas, “Single Sample Face Recognition Using Convolutional Neural Networks for Automated Attendance Systems,” 4th International Conference on Intelligent Computing in Data Sciences (ICDS2020), Fez, Morocco, 2020.