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...

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Bibliographic Details
Published in:Applied artificial intelligence 2022-12, Vol.36 (1)
Main Authors: Tran, Quan M., Pham, Vuong T., Nga, Duong Thi Thuy, The Bao, Pham
Format: Article
Language:English
Subjects:
Artificial neural networks
Face recognition
Image classification
Image resolution
Interpolation
Machine learning
Object recognition
Optimization
Parameters
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Summary: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.
ISSN:0883-9514
1087-6545
DOI:10.1080/08839514.2021.2012982