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Journal articles on the topic 'Low Resolution Face Recognition'

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Author: Grafiati

Published: 6 September 2023

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1

Chen, Jin, Jun Chen, Zheng Wang, Chao Liang, and Chia-Wen Lin. "Identity-Aware Face Super-Resolution for Low-Resolution Face Recognition." IEEE Signal Processing Letters 27 (2020): 645–49. http://dx.doi.org/10.1109/lsp.2020.2986942.

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2

Wang, Zhifei, Zhenjiang Miao, Q. M. Jonathan Wu, Yanli Wan, and Zhen Tang. "Low-resolution face recognition: a review." Visual Computer 30, no. 4 (August 6, 2013): 359–86. http://dx.doi.org/10.1007/s00371-013-0861-x.

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3

Zou, W. W. W., and P. C. Yuen. "Very Low Resolution Face Recognition Problem." IEEE Transactions on Image Processing 21, no. 1 (January 2012): 327–40. http://dx.doi.org/10.1109/tip.2011.2162423.

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4

Hong, Sungeun, and Jongbin Ryu. "Unsupervised Face Domain Transfer for Low-Resolution Face Recognition." IEEE Signal Processing Letters 27 (2020): 156–60. http://dx.doi.org/10.1109/lsp.2019.2963001.

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5

Xu, Xiang, Wanquan Liu, and Ling Li. "Low Resolution Face Recognition in Surveillance Systems." Journal of Computer and Communications 02, no. 02 (2014): 70–77. http://dx.doi.org/10.4236/jcc.2014.22013.

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6

Mostafa, Eslam, Riad Hammoud, Asem Ali, and Aly Farag. "Face recognition in low resolution thermal images." Computer Vision and Image Understanding 117, no. 12 (December 2013): 1689–94. http://dx.doi.org/10.1016/j.cviu.2013.07.010.

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7

Han, Feng, Xudong Wang, Furao Shen, and Jian Zhao. "C-Face: Using Compare Face on Face Hallucination for Low-Resolution Face Recognition." Journal of Artificial Intelligence Research 74 (August 16, 2022): 1715–37. http://dx.doi.org/10.1613/jair.1.13816.

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Face hallucination is a task of generating high-resolution (HR) face images from low-resolution (LR) inputs, which is a subfield of the general image super-resolution. However, most of the previous methods only consider the visual effect, ignoring how to maintain the identity of the face. In this work, we propose a novel face hallucination model, called C-Face network, which can generate HR images with high visual quality while preserving the identity information. A face recognition network is used to extract the identity features in the training process. In order to make the reconstructed face images keep the identity information to a great extent, a novel metric, i.e., C-Face loss, is proposed. We also propose a new training algorithm to deal with the convergence problem. Moreover, since our work mainly focuses on the recognition accuracy of the output, we integrate face recognition into the face hallucination process which ensures that the model can be used in real scenarios. Extensive experiments on two large scale face datasets demonstrate that our C-Face network has the best performance compared with other state-of-the-art methods.

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8

Li, Sisi, Zhonghua Liu, Di Wu, Hua Huo, Haijun Wang, and Kaibing Zhang. "Low-resolution face recognition based on feature-mapping face hallucination." Computers and Electrical Engineering 101 (July 2022): 108136. http://dx.doi.org/10.1016/j.compeleceng.2022.108136.

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9

Peng, Yuxi, Luuk Spreeuwers, and Raymond Veldhuis. "Low‐resolution face alignment and recognition using mixed‐resolution classifiers." IET Biometrics 6, no. 6 (April 24, 2017): 418–28. http://dx.doi.org/10.1049/iet-bmt.2016.0026.

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10

Maity, Sayan, Mohamed Abdel-Mottaleb, and Shihab S. Asfour. "Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video." Electronics 10, no. 9 (April 24, 2021): 1013. http://dx.doi.org/10.3390/electronics10091013.

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Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

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11

Ge, Shiming, Shengwei Zhao, Chenyu Li, Yu Zhang, and Jia Li. "Efficient Low-Resolution Face Recognition via Bridge Distillation." IEEE Transactions on Image Processing 29 (2020): 6898–908. http://dx.doi.org/10.1109/tip.2020.2995049.

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12

Biswas, Soma, Gaurav Aggarwal, Patrick J. Flynn, and Kevin W. Bowyer. "Pose-Robust Recognition of Low-Resolution Face Images." IEEE Transactions on Pattern Analysis and Machine Intelligence 35, no. 12 (December 2013): 3037–49. http://dx.doi.org/10.1109/tpami.2013.68.

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13

Yang, Fuwei, Wenming Yang, Riqiang Gao, and Qingmin Liao. "Discriminative Multidimensional Scaling for Low-Resolution Face Recognition." IEEE Signal Processing Letters 25, no. 3 (March 2018): 388–92. http://dx.doi.org/10.1109/lsp.2017.2746658.

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14

Lu, Ze, Xudong Jiang, and Alex Kot. "Deep Coupled ResNet for Low-Resolution Face Recognition." IEEE Signal Processing Letters 25, no. 4 (April 2018): 526–30. http://dx.doi.org/10.1109/lsp.2018.2810121.

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15

Wang, Zhifei, Zhenjiang Miao, Yanli Wan, and Zhen Tang. "Coupled cross-regression for low-resolution face recognition." Journal of Electronic Imaging 22, no. 2 (May 22, 2013): 023015. http://dx.doi.org/10.1117/1.jei.22.2.023015.

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16

Choi, Sang-Il. "Feature Generation Method for Low-Resolution Face Recognition." Journal of Korea Multimedia Society 18, no. 9 (September 30, 2015): 1039–46. http://dx.doi.org/10.9717/kmms.2015.18.9.1039.

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17

Zaeri, Naser. "Compressed phase component for low resolution face recognition." International Journal of Biometrics 6, no. 4 (2014): 387. http://dx.doi.org/10.1504/ijbm.2014.067139.

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18

Ben, X. Y., M. Y. Jiang, Y. J. Wu, and W. X. Meng. "Gait feature coupling for low-resolution face recognition." Electronics Letters 48, no. 9 (2012): 488. http://dx.doi.org/10.1049/el.2011.4041.

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19

Suharjito, Suharjito, and Atria Dika Puspita. "Face Recognition on Low Resolution Face Image With TBE-CNN Architecture." Advances in Science, Technology and Engineering Systems Journal 5, no. 2 (2020): 730–38. http://dx.doi.org/10.25046/aj050291.

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20

Wang, Zhifei, Zhenjiang Miao, Yanli Wan, and Zhen Tang. "Kernel Coupled Cross-Regression for Low-Resolution Face Recognition." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/153790.

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Low resolution (LR) in face recognition (FR) surveillance applications will cause the problem of dimensional mismatch between LR image and its high-resolution (HR) template. In this paper, a novel method called kernel coupled cross-regression (KCCR) is proposed to deal with this problem. Instead of processing in the original observing space directly, KCCR projects LR and HR face images into a unified nonlinear embedding feature space using kernel coupled mappings and graph embedding. Spectral regression is further employed to improve the generalization performance and reduce the time complexity. Meanwhile, cross-regression is developed to fully utilize the HR embedding to increase the information of the LR space, thus to improve the recognition performance. Experiments on the FERET and CMU PIE face database show that KCCR outperforms the existing structure-based methods in terms of recognition rate as well as time complexity.

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21

Chu, Yongjie, Touqeer Ahmad, George Bebis, and Lindu Zhao. "Low-resolution face recognition with single sample per person." Signal Processing 141 (December 2017): 144–57. http://dx.doi.org/10.1016/j.sigpro.2017.05.012.

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22

Zhang, Peng, Xianye Ben, Wei Jiang, Rui Yan, and Yiming Zhang. "Coupled marginal discriminant mappings for low-resolution face recognition." Optik 126, no. 23 (December 2015): 4352–57. http://dx.doi.org/10.1016/j.ijleo.2015.08.138.

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23

Chuan-Xian Ren, Dao-Qing Dai, and Hong Yan. "Coupled Kernel Embedding for Low-Resolution Face Image Recognition." IEEE Transactions on Image Processing 21, no. 8 (August 2012): 3770–83. http://dx.doi.org/10.1109/tip.2012.2192285.

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24

Bo Li, Hong Chang, Shiguang Shan, and Xilin Chen. "Low-Resolution Face Recognition via Coupled Locality Preserving Mappings." IEEE Signal Processing Letters 17, no. 1 (January 2010): 20–23. http://dx.doi.org/10.1109/lsp.2009.2031705.

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25

Rajput, Shyam Singh, and K. V. Arya. "A robust face super-resolution algorithm and its application in low-resolution face recognition system." Multimedia Tools and Applications 79, no. 33-34 (June 15, 2020): 23909–34. http://dx.doi.org/10.1007/s11042-020-09072-5.

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26

Saad Shakeel, M., Kin-Man Lam, and Shun-Cheung Lai. "Learning sparse discriminant low-rank features for low-resolution face recognition." Journal of Visual Communication and Image Representation 63 (August 2019): 102590. http://dx.doi.org/10.1016/j.jvcir.2019.102590.

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27

Shakya, Subarna. "Multi Distance Face Recognition of Eye Localization with Modified Gaussian Derivative Filter." September 2021 3, no. 3 (September 10, 2021): 240–54. http://dx.doi.org/10.36548/jiip.2021.3.006.

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Face recognition at a distance (FRAD) is one of the most difficult types of face recognition applications, particularly at a distance. Due to the poor resolution of facial image, it is difficult to identify faces from a distance. Recently, while recording individuals, the camera view is broad and just a small portion of a person's face is visible in the image. To ensure that the facial image has a low resolution, which deteriorates both face detection and identification engines, the facial image is constantly at low resolution. As an immediate solution, employing a high-definition camera is considered as a simple and practical approach to improve the reliability of algorithm and perform well on low-resolution facial images. While facial detection will be somewhat decreased, a picture with higher quality will result in a slower face detection rate. The proposed work aims to recognize faces with good accuracy even at a distance. The eye localization works for the face and eye location in the face of a human being with varied sizes at multiple distances. This process is used to detect the face quickly with a comparatively high accuracy. The Gaussian derivative filter is used to reduce the feature size in the storage element, which improves the speed of the recognition ratio. Besides, the proposed work includes benchmark datasets to evaluate the recognition process. As a result, the proposed system has achieved a 93.24% average accuracy of face recognition.

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28

Li, Jiadi, Zhenxue Chen, and Chengyun Liu. "Low-Resolution Face Recognition of Multi-Scale Blocking CS-LBP and Weighted PCA." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 08 (July 17, 2016): 1656005. http://dx.doi.org/10.1142/s021800141656005x.

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A novel method is proposed in this paper to improve the recognition accuracy of Local Binary Pattern (LBP) on low-resolution face recognition. More precise descriptors and effectively face features can be extracted by combining multi-scale blocking center symmetric local binary pattern (CS-LBP) based on Gaussian pyramids and weighted principal component analysis (PCA) on low-resolution condition. Firstly, the features statistical histograms of face images are calculated by multi-scale blocking CS-LBP operator. Secondly, the stronger classification and lower dimension features can be got by applying weighted PCA algorithm. Finally, the different classifiers are used to select the optimal classification categories of low-resolution face set and calculate the recognition rate. The results in the ORL human face databases show that recognition rate can get 89.38% when the resolution of face image drops to 12[Formula: see text]10 pixel and basically satisfy the practical requirements of recognition. The further comparison of other descriptors and experiments from videos proved that the novel algorithm can improve recognition accuracy.

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29

Tabejamaat, Mohsen, Abdolmajid Mousavi, and Marina L. Gavrilova. "Local Comparative Decimal Pattern for Face Recognition." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (March 20, 2020): 2056006. http://dx.doi.org/10.1142/s0218001420560066.

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Rapid growth of social networks has provided an extraordinary medium to share a large volume of photographs online. This calls for designing efficient face recognition techniques that are applicable to images with low resolutions and arbitrary poses. This paper proposes a new pose invariant face recognition method for low resolution images using only a single training sample. A 3D model, reconstructed using Generic Elastic Model (3D GEM) from a frontal view training sample, is used to generate a set of nonfrontal gallery face images. The face region of the nonfrontal query sample is then extracted using the same landmark detection technique as in the 3D GEM algorithm. Afterwards, a novel texture representation technique called Local Comparative Decimal Pattern (LCDP) is proposed to extract features from each of the training and query samples. A set of experimental results on the ORL, Georgia Tech (GT), and LFW face databases demonstrates the efficiency of the proposed method compared to other state-of-the-art approaches.

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30

Xue, Shan, and Hong Zhu. "Low-resolution and open-set face recognition via recursive label propagation based on statistical classification." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 02 (March 2019): 1940002. http://dx.doi.org/10.1142/s0219691319400022.

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In video surveillance, the captured face images are usually suffered from low-resolution (LR), besides, not all the probe images have mates in the gallery under the premise that only a single frontal high-resolution (HR) face image per subject. To address this problem, a novel face recognition framework called recursive label propagation based on statistical classification (ReLPBSC) has been proposed in this paper. Firstly, we employ VGG to extract robust discriminative feature vectors to represent each face. Then we select the corresponding LR face in the probe for each HR gallery face by similarity. Based on the picked HR–LR pairs, ReLPBSC is implemented for recognition. The main contributions of the proposed approach are as follows: (i) Inspired by substantial achievements of deep learning methods, VGG is adopted to achieve discriminative representation for LR faces to avoid the super-resolution steps; (ii) the accepted and rejected threshold parameters, which are not fixed in face recognition, can be achieved with ReLPBSC adaptively; (iii) the unreliable subjects never enrolled in the gallery can be rejected automatically with designed methods. Experimental results in [Formula: see text] pixels resolution show that the proposed method can achieve 86.64% recall rate while keeping 100% precision.

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31

Li, Peiying, Shikui Tu, and Lei Xu. "Deep Rival Penalized Competitive Learning for low-resolution face recognition." Neural Networks 148 (April 2022): 183–93. http://dx.doi.org/10.1016/j.neunet.2022.01.009.

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32

Mudunuri, Sivaram Prasad, and Soma Biswas. "Low Resolution Face Recognition Across Variations in Pose and Illumination." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 5 (May 1, 2016): 1034–40. http://dx.doi.org/10.1109/tpami.2015.2469282.

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33

Lee, Sang-Woong, Jooyoung Park, and Seong-Whan Lee. "Low resolution face recognition based on support vector data description." Pattern Recognition 39, no. 9 (September 2006): 1809–12. http://dx.doi.org/10.1016/j.patcog.2006.04.033.

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34

Zheng, Dongdong, Kaibing Zhang, Jian Lu, Junfeng Jing, and Zenggang Xiong. "Active Discriminative Cross-Domain Alignment for Low-Resolution Face Recognition." IEEE Access 8 (2020): 97503–15. http://dx.doi.org/10.1109/access.2020.2996796.

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35

Peng, Yuxi, Luuk J. Spreeuwers, and Raymond N. J. Veldhuis. "Low‐resolution face recognition and the importance of proper alignment." IET Biometrics 8, no. 4 (February 20, 2019): 267–76. http://dx.doi.org/10.1049/iet-bmt.2018.5008.

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36

Chu, Yongjie, Yong Zhao, Touqeer Ahmad, and Lindu Zhao. "Low-Resolution Face Recognition with Single Sample per Person via Domain Adaptation." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 05 (April 8, 2019): 1956005. http://dx.doi.org/10.1142/s0218001419560056.

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Numerous low-resolution (LR) face images are captured by a growing number of surveillance cameras nowadays. In some particular applications, such as suspect identification, it is required to recognize an LR face image captured by the surveillance camera using only one high-resolution (HR) profile face image on the ID card. This leads to LR face recognition with single sample per person (SSPP), which is more challenging than conventional LR face recognition or SSPP face recognition. To address this tough problem, we propose a Boosted Coupled Marginal Fisher Analysis (CMFA) approach, which unites domain adaptation and coupled mappings. An auxiliary database containing multiple HR and LR samples is introduced to explore more discriminative information, and locality preserving domain adaption (LPDA) is designed to realize good domain adaptation between SSPP training set (target domain) and auxiliary database (source domain). We perform LPDA on HR and LR images in both domains, then in the domain adaptation space we apply CMFA to learn the discriminative coupled mappings for classification. The learned coupled mappings embed knowledge from the auxiliary dataset, thus their discriminative ability is superior. We extensively evaluate the proposed method on FERET, LFW and SCface database, the promising results demonstrate its effectiveness on LR face recognition with SSPP.

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37

Thomas, Renjith, and M. J. S. Rangachar. "Fractional Bat and Multi-Kernel-Based Spherical SVM for Low Resolution Face Recognition." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 08 (May 9, 2017): 1756014. http://dx.doi.org/10.1142/s0218001417560146.

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Face recognition is an important aspect of the biometric surveillance system. Generally, face recognition is a type of biometric system that can identify a specific individual by analyzing and comparing patterns in the facial image. Face recognition has distinct advantage over other biometrics is noncontact process. It has a wide variety of applications in both the law enforcement and nonlaw enforcement. While using the low resolution face images, the resolution of the image gets degraded. In this paper, to enhance the performance rate for low resolution image, the fractional Bat algorithm and multi-kernel-based spherical SVM classifier is proposed. Initially, the low resolution image is converted into the high resolution images by the kernel regression method. The GWTM process is utilized for the feature extraction by the Gabor filter, wavelet transform and local binary pattern (texture descriptors). Then, the super resolution images are applied to the feature level fusion by using the fractional Bat algorithm which comprises of fractional theory and Bat algorithm. Finally, the multi-kernel-based spherical SVM classifier is introduced for the recognition of feature images. The experimental results and performance analysis evaluated by the comparison metrics are FAR, FRR and Accuracy with existing systems. Thus, the outcome of our proposed system achieves the highest accuracy of 95% based on the training data samples, stopping criterion and number of draw attempts.

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38

Gao, Guangwei, Zangyi Hu, Pu Huang, Meng Yang, Quan Zhou, Songsong Wu, and Dong Yue. "Robust low-resolution face recognition via low-rank representation and locality-constrained regression." Computers & Electrical Engineering 70 (August 2018): 968–77. http://dx.doi.org/10.1016/j.compeleceng.2018.02.040.

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39

Moon, Hae-Min, Min-Gu Kim, Ju-Hyun Shin, and Sung Bum Pan. "Multiresolution Face Recognition through Virtual Faces Generation Using a Single Image for One Person." Wireless Communications and Mobile Computing 2018 (November 11, 2018): 1–8. http://dx.doi.org/10.1155/2018/7584942.

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In recent years, various studies have been conducted to provide a real-time service based on face recognition in Internet of things environments such as in a smart home environment. In particular, face recognition in a network-based surveillance camera environment can significantly change the performance or utilization of face recognition technology because the size of image information to be transmitted varies depending on the communication capabilities. In this paper, we propose a multiresolution face recognition method that uses virtual facial images by distance as learning to solve the problem of low recognition rate caused by communication, camera, and distance change. Face images for each virtual distance are generated through clarity and image degradation for each resolution, using a single high-resolution face image. The proposed method achieved a performance that was 5.9% more accurate than methods using MPCA and SVM, when LDA and the Euclidean distance were employed for a DB that was configured using faces that were acquired from the real environments of five different streets.

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40

Ebrahimpour, Reza, Naser Sadeghnejad, Saeed Masoudnia, and Seyed Ali Asghar Abbaszadeh Arani. "Boosted Pre-loaded Mixture of Experts for low-resolution face recognition." International Journal of Hybrid Intelligent Systems 9, no. 3 (October 24, 2012): 145–58. http://dx.doi.org/10.3233/his-2012-0153.

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41

Pei, Jihong, Yebin Chen, Yang Zhao, Chao Wang, and Xuan Yang. "Self-adjusting multilayer nonlinear coupled mapping for low-resolution face recognition." Applied Soft Computing 129 (November 2022): 109566. http://dx.doi.org/10.1016/j.asoc.2022.109566.

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42

Senthilsingh, C., and M. Manikandan. "A Novel Algorithm for Face Recognition From Very Low Resolution Images." Journal of Electrical Engineering and Technology 10, no. 2 (March 1, 2015): 659–69. http://dx.doi.org/10.5370/jeet.2015.10.2.659.

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43

Ge, Shiming, Shengwei Zhao, Chenyu Li, and Jia Li. "Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation." IEEE Transactions on Image Processing 28, no. 4 (April 2019): 2051–62. http://dx.doi.org/10.1109/tip.2018.2883743.

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44

Wang, Zhenyu, Wankou Yang, and Xianye Ben. "Low-resolution degradation face recognition over long distance based on CCA." Neural Computing and Applications 26, no. 7 (February 4, 2015): 1645–52. http://dx.doi.org/10.1007/s00521-015-1834-y.

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45

Zhou, Juan, and Yong Ping Li. "Low-Resolution Range Data Surface Matching for 3D Face Verification." Advanced Materials Research 468-471 (February 2012): 1957–61. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.1957.

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In this paper, we presented a novel approach for automatic 3D face verification by range data surface matching. The method consists of range data registration and comparison. There are three steps in registration procedure: the coarse step conducting the normalization by exploiting a priori knowledge of the human face and facial features to make faces have the similar attitude; the next step considering the existence of holes in the low-resolution range data will undermining the recognition results, we presented a novel approach for holes filling to improve the range data quality; and the fine step aligning the input data with the model in the database by the Delaunay-Iterative Closest Point (D-ICP) algorithm. During the face comparison, a Modified Hausdorff Distance (MHD) is employed as the similarity metrics. The experiments are carried out on a database with 30 individuals, and the best EER of 1.667% is achieved.

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46

George, Anitta, Krishnendu K A, Anusree K, Adira Suresh Nair, and Hari Shree. "Criminal Face Recognition Using GAN." International Journal of Innovative Science and Research Technology 5, no. 6 (July 18, 2020): 1526–28. http://dx.doi.org/10.38124/ijisrt20jun1116.

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Forensics and security at present often use low technological resources. Security measures often fail to update with the upcoming technology. This project is based on implementing an automatic face recognition of criminals or specific targets using machine-learning approach. Given a set of features to a Generative Adversarial Network(GAN), the algorithm generates an image of the target with the specified feature set. The input to the machine can either be a given set of features or a set of portraits varying from frontals to side profiles from which these features can be extracted. The accuracy of the system is directly proportional to the number of epochs trained in the network. The generated output image can vary from primitive, low resolution images to high quality images where features are more recognizable. This is then compared with a predefined database of existing people. Thus, the target can immediately be recognized with the generation of an artificial image with the given biometric feature set, which will be again compared by a discriminator network to check the true identity of the target.

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47

Zhang, Ziwei, Yangjing Shi, Xiaoshi Zhou, Hongfei Kan, and Juan Wen. "Shuffle block SRGAN for face image super-resolution reconstruction." Measurement and Control 53, no. 7-8 (August 2020): 1429–39. http://dx.doi.org/10.1177/0020294020944969.

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When low-resolution face images are used for face recognition, the model accuracy is substantially decreased. How to recover high-resolution face features from low-resolution images precisely and efficiently is an essential subtask in face recognition. In this study, we introduce shuffle block SRGAN, a new image super-resolution network inspired by the SRGAN structure. By replacing the residual blocks with shuffle blocks, we can achieve efficient super-resolution reconstruction. Furthermore, by considering the generated image quality in the loss function, we can obtain more realistic super-resolution images. We train and test SB-SRGAN in three public face image datasets and use transfer learning strategy during the training process. The experimental results show that shuffle block SRGAN can achieve desirable image super-resolution performance with respect to visual effect as well as the peak signal-to-noise ratio and structure similarity index method metrics, compared with the performance attained by the other chosen deep-leaning models.

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48

R., Vijaya Kumar H., and M. Mathivanan. "A novel hybrid face recognition framework based on a low-resolution camera for biometric applications." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 2 (November 1, 2021): 853. http://dx.doi.org/10.11591/ijeecs.v24.i2.pp853-863.

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In research work, human face recognition is an essential biometric symbol persistently continued so far due to its different levels of applications in society. Since the appearance of the human faces can have many variations due to issues like the effect of illumination, expression and face pose. These differences are correlated with one another, which results in a helpless ability to recognize a particular person's face. The motivation behind our work in this paper is to give a new framework for face recognition based on frequency analysis that contributes to solving the distinguishing proof issues with enormous varieties of boundaries like the effect of illumination, expression, and face pose. Here three algorithms combined for provable results: i) Difference of Gaussian filtered discrete wavelet transform (DDWT) for feature extraction; ii) Log Gabor (LG) filter for feature extraction; and iv) Multiclass support vector machine classifier, where feature coefficients of DDWT and LG filter are fused for classification and parameters evaluation. The evaluation of our experiment is carried out on a large database consisting of 15 persons of each 200-face image which are captured using a 5-megapixel low-resolution web camera and yielding satisfactory results on various parameters compared to existing methods.

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Nam, Gi, Heeseung Choi, Junghyun Cho, and Ig-Jae Kim. "PSI-CNN: A Pyramid-Based Scale-Invariant CNN Architecture for Face Recognition Robust to Various Image Resolutions." Applied Sciences 8, no. 9 (September 5, 2018): 1561. http://dx.doi.org/10.3390/app8091561.

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Abstract:

Face recognition is one research area that has benefited from the recent popularity of deep learning, namely the convolutional neural network (CNN) model. Nevertheless, the recognition performance is still compromised by the model’s dependency on the scale of input images and the limited number of feature maps in each layer of the network. To circumvent these issues, we propose PSI-CNN, a generic pyramid-based scale-invariant CNN architecture which additionally extracts untrained feature maps across multiple image resolutions, thereby allowing the network to learn scale-independent information and improving the recognition performance on low resolution images. Experimental results on the LFW dataset and our own CCTV database show PSI-CNN consistently outperforming the widely-adopted VGG face model in terms of face matching accuracy.

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anto, Yuli, Nurha sanah, Risma Yulistiani, and 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 70, no. 3 (March 25, 2022): 1–12. http://dx.doi.org/10.14445/22315381/ijett-v70i3p201.

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