Face Image Super-Resolution Using Combination of Max-Feature-Map and CMU-Net to Enhance Low-Resolution Face Recognition
Face Image Super-Resolution Using Combination of Max-Feature-Map and CMU-Net to Enhance Low-Resolution Face Recognition |
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© 2022 by IJETT Journal | ||
Volume-70 Issue-3 |
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Year of Publication : 2022 | ||
Authors : Yulianto, Nurhasanah, Risma Yulistiani, Gede Putra Kusuma |
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https://doi.org/10.14445/22315381/IJETT-V70I3P201 |
How to Cite?
Yulianto, Nurhasanah, Risma Yulistiani, Gede Putra Kusuma, "Face Image Super-Resolution Using Combination of Max-Feature-Map and CMU-Net to Enhance Low-Resolution Face Recognition," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 1-12, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I3P201
Abstract
The far distance of the camera while taking a picture makes the visual image look blurred. In deep learning, the blurred image eliminates the classification accuracy. The decreasing classification accuracy is caused by the loss of detailed information on High-Resolution (HR) images. The Generative Adversarial Networks (GAN) for Super-Resolution (SR) can overcome the problem and deal with the blurred images. In the usual term, GAN is used for reconstructing the quality of images visually. While not all GANs may be utilized to increase classification accuracy, SR image findings are highly realistic. This study offers a CMU-Net MFM SR technique based on this challenge, which combines a modified U-Net plus a Max-Feature-Map (MFM) module and a mix of BCE loss, Cosine matrix loss, and magnitude loss to restore the identity result of the SR image. With a classification accuracy of 78.45%, the experimental results of this method employing the LFW (Labelled Face in the Wild) dataset may be utilized to boost the image resolution from 8 x 8 pixels to 64 x 64 pixels.
Keywords
Generative Adversarial Networks, U-Net Model, Super-Resolution, Convolution Neural Networks, Low-Resolution Face Recognition.
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