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• W2965774906 abstract "We propose a novel coupled mappings method for low resolution face recognition using deep convolutional neural networks (DCNNs). The proposed architecture consists of two branches of DCNNs to map the high and low resolution face images into a common space with nonlinear transformations. The branch corresponding to transformation of high resolution images consists of 14 layers and the other branch which maps the low resolution face images to the common space includes a 5-layer super-resolution network connected to a 14-layer network. The distance between the features of corresponding high and low resolution images are backpropagated to train the networks. Our proposed method is evaluated on FERET, LFW, and MBGC datasets and compared with state-of-the-art competing methods. Our extensive experimental evaluations show that the proposed method significantly improves the recognition performance especially for very low resolution probe face images (5% improvement in recognition accuracy). Furthermore, it can reconstruct a high resolution image from its corresponding low resolution probe image which is comparable with the state-of-the-art super-resolution methods in terms of visual quality." @default.
• W2965774906 created "2019-08-13" @default.
• W2965774906 creator A5012687122 @default.
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• W2965774906 date "2020-01-01" @default.
• W2965774906 modified "2023-09-27" @default.
• W2965774906 title "Low resolution face recognition using a two-branch deep convolutional neural network architecture" @default.
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• W2965774906 doi "https://doi.org/10.1016/j.eswa.2019.112854" @default.
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