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

© 2022 by IJETT Journal
Volume-70 Issue-3
Year of Publication : 2022
Authors : Yulianto, Nurhasanah, Risma Yulistiani, Gede Putra Kusuma

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

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.

Generative Adversarial Networks, U-Net Model, Super-Resolution, Convolution Neural Networks, Low-Resolution Face Recognition.

[1] T. Gwyn, K. Roy, and M. Atay., Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study, Future Internet, 13(7) ( 2021) doi: 10.3390/fi13070164.
[2] Asma Shaikh, Aditi Mhadgut, Apurva Prasad, Bhagyashree Shinde, and Rohan Pandita, Two-way Credit Card Authentication With Face Recognition Using Webcam, International Journal of Engineering Trends and Technology (IJETT), 67(5) (2019) 160–162.
[3] O. A. Aghdam, B. Bozorgtabar, H. K. Ekenel, and J.-P. Thiran, Exploring factors for improving low-resolution face recognition, in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), (2019) 2363–2370. doi: 10.1109/CVPRW.2019.00290.
[4] S.-C. Lai, M. Kong, K.-M. Lam and D. Li, High-Resolution Face Recognition Via Deep Pore-Feature Matching, in 2019 IEEE International Conference on Image Processing (ICIP), (2019) 3477–3481. doi: 10.1109/ICIP.2019.8803686.
[5] M. Rouhsedaghat, Y. Wang, S. Hu, S. You, and C.-C. J. Kuo, Low-resolution face recognition in resource-constrained environments, Pattern Recognition Letters, 49 (2021) 193–199. doi: 10.1016/j.patrec.2021.05.009.
[6] Z. Zhang, X. Pan, S. Jiang, and P. Zhao, High-quality face image generation based on generative adversarial networks, Journal of Visual Communication and Image Representation, 71 (2020) 1178–1182. doi: 10.1016/j.jvcir.2019.102719.
[7] S. D. Indradi, A. Arifianto, and K. N. Ramadhani, “Face image super-resolution using inception residual network and GAN framework, 2019 7th International Conference on Information and Communication Technology, ICoICT , 1 (2019) 1-6. doi: 10.1109/ICoICT.2019.8835253.
[8] J. Chen, J. Chen, Z. Wang, C. Liang, and C. Lin, Identity-Aware Face Super-Resolution for Low-Resolution Face Recognition, IEEE Signal Processing Letters, 9908( c) (2020) 1–5. doi: 10.1109/LSP.2020.2986942.
[9] D. Khaledyan, A. Amirany, K. Jafari, M. H. Moaiyeri, A. Z. Khuzani, and N. Mashhadi, Low-Cost Implementation of Bilinear and Bicubic Image Interpolation for Real-Time Image Super-Resolution, in 2020 IEEE Global Humanitarian Technology Conference (GHTC), (2020) 1–5. doi: 10.1109/GHTC46280.2020.9342625.
[10] X. Li, N. Dong, J. Huang, L. Zhuo, and J. Li, A discriminative self-attention cycle GAN for face super-resolution and recognition, IET Image Processing, 15(11) (2021) 2614–2628. doi: 10.1049/ipr2.12250.
[11] C.-C. Hsu, C.-W. Lin, W.-T. Su, and G. Cheung, Sigan: Siamese generative adversarial network for identity-preserving face hallucination, IEEE Transactions on Image Processing, 28(12) (2019) 6225–6236. doi: 10.1109/TIP.2019.2924554.
[12] X. Wu, R. He, Z. Sun, and T. Tan, “A light CNN for deep face representation with noisy labels,” IEEE Transactions on Information Forensics and Security, 13(11) (2018) 2884–2896, 2018, doi: 10.1109/TIP.2018.2883743.
[13] J. He, J. Zheng, Y. Shen, Y. Guo, and H. Zhou, Facial Image Synthesis and Super-Resolution With Stacked Generative Adversarial Network, Neurocomputing, 402 (2020) 359–365. doi: 10.1016/j.neucom.2020.03.107.
[14] S. Ge, S. Zhao, C. Li, and J. Li, Low-resolution face recognition in the wild via selective knowledge distillation, arXiv, 28(4) (2018) 2051–2062. doi: 10.1109/TIP.2018.2883743.
[15] T. Lu, X. Chen, Y. Zhang, C. Chen, and Z. Xiong, SLR: Semi-Coupled Locality Constrained Representation for Very Low-Resolution Face Recognition and Super-Resolution, IEEE Access, 6 (2018) 56269–56281. doi: 10.1109/ACCESS.2018.2872761.
[16] K. Grm, W. J. Scheirer, and V. Štruc, Face hallucination using cascaded super-resolution and identity priors., IEEE Transactions on Image Processing, 29 (2019) 2150–2165. doi: 10.1109/TIP.2019.2945835.
[17] O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for biomedical image segmentation, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9351 (2015) 234–241. doi: 10.1007/978-3-319-24574-4_28.
[18] P. Isola, J. Y. Zhu, T. Zhou, and A. A. Efros, Image-to-image translation with conditional adversarial networks, Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, (2017) (2017) 5967–5976. doi: 10.1109/CVPR.2017.632.
[19] M. Lucic, K. Kurach, M. Michalski, S. Gelly, and O. Bousquet, Are gans created equal? a large-scale study, arXiv preprint arXiv:1711.10337, (2017).
[20] M. M. Majdabadi and S. B. Ko, MSG-CapsGAN: Multi-Scale Gradient Capsule GAN for Face Super-Resolution, 2020 International Conference on Electronics, Information, and Communication, ICEIC (2020) 10–12. doi: 10.1109/ICEIC49074.2020.9051244.
[21] Nuno-Maganda and M. O. Arias-Estrada, Real-time FPGA-based architecture for bicubic interpolation: an application for digital image scaling, in 2005 International Conference on Reconfigurable Computing and FPGAs (ReConFig’05), (2005) 1. doi: 10.1109/RECONFIG.2005.34.
[22] X. Wei et al., Building Outline Extraction Directly Using the U2-Net Semantic Segmentation Model from High-Resolution Aerial Images and a Comparison Study, Remote Sensing, 13(16) (2021). doi: 10.3390/rs13163187.
[23] Sunil Pandey, Naresh Kumar Nagwani, and Shrish Verma, Analysis and Design of High-Performance Deep Learning Algorithm: Convolutional Neural Networks, International Journal of Engineering Trends and Technology (IJETT), 69(6) (2021) 216–224.
[24] Y. Shi, Q. Li, and X. X. Zhu, Building Footprint Generation Using Improved Generative Adversarial Networks, IEEE Geoscience and Remote Sensing Letters, 16(4) (2019) 603–607. doi: 10.1109/LGRS.2018.2878486.
[25] Yasser Mohammad Al-Sharo, Amer Tahseen Abu-Jassar, Svitlana Sotnik, and Vyacheslav Lyashenko, Neural Networks As A Tool For Pattern Recognition of Fasteners, International Journal of Engineering Trends and Technology (IJETT), 69(10) (2021) 151–160.
[26] G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, Oct. (2008).
[27] K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, Joint Face Detection and Alignment Using Multi-task Cascaded Convolutional Networks, IEEE Signal Processing Letters, 23(10) (2016) 1499–1503. doi: 10.1109/LSP.2016.2603342.
[28] A. Rai, V. Chudasama, K. Upla, K. Raja, R. Ramachandra, and C. Busch, ComSupResNet: A compact super-resolution network for low-resolution face images, in 2020 8th International Workshop on Biometrics and Forensics (IWBF), (2020) 1–6. doi:10.1109/IWBF49977.2020.9107946.