Evaluating the Efficacy of Small Face Recognition by Convolutional Neural Networks with Interpolation Based on Auto-adjusted Parameters and Transfer Learning
| Autor: | Quan M. Tran, Vuong T. Pham, Duong Thi Thuy Nga, Pham The Bao |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Applied Artificial Intelligence, Vol 36, Iss 1 (2022) |
| Druh dokumentu: | article |
| ISSN: | 0883-9514 1087-6545 08839514 |
| DOI: | 10.1080/08839514.2021.2012982 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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