Evaluating the Efficacy of Small Face Recognition by Convolutional Neural Networks with Interpolation Based on Auto-adjusted Parameters and Transfer Learning

In this work, we propose a new approach for face recognition using low-resolution images. By cleverly combining conventional interpolation methods with the state-of-the-art classification approach, i.e. convolutional neural network, we introduce a new approach to efficiently leverage low-resolution images in classification task, especially in face recognition. Besides, we also do experiments on some recent popular methods, our approach outperforms some of them. Additionally, we propose a specific transfer learning strategy based on the preexisting well-known concept dedicated to low-resolution transfer learning. It boosts performance and reduces training time significantly. We also investigate on scalability by applying Bayesian optimization for hyper-parameter search. Therefore, our approach is able to be widely applied in many kinds of datasets and low-resolution classification tasks due to automatically seeking optimal hyper-parameters, which makes our method competitive to others.