scholarly journals Low Resolution Face Recognition Using Generative Adversarial Network (GAN)

2020 ◽  
Author(s):  
Howard Martin ◽  
Suharjito
Keyword(s):  
Face Recognition ◽  
Super Resolution ◽  
Image Data ◽  
Face Image ◽  
Image Resolution ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Resolution Image ◽  
Surveillance Camera ◽  
Adversarial Network

Abstract Face recognition has a lot of use on smartphone authentication, finding people, etc. Nowadays, face recognition with a constrained environment has achieved very good performance on accuracy. However, the accuracy of existing face recognition methods will gradually decrease when using a dataset with an unconstrained environment. Face image with an unconstrained environment is usually taken from a surveillance camera. In general, surveillance cameras will be placed on the corner of a room or even on the street. So, the image resolution will be low. Low-resolution image will cause the face very hard to be recognized and the accuracy will eventually decrease. That is the main reason why increasing the accuracy of the Low-Resolution Face Recognition (LRFR) problem is still challenging. This research aimed to solve the Low-Resolution Face Recognition (LRFR) problem. The datasets are YouTube Faces Database (YTF) and Labelled Faces in The Wild (LFW). In this research, face image resolution would be decreased using bicubic linear and became the low-resolution image data. Then super resolution methods as the preprocessing step would increase the image resolution. Super resolution methods used in this research are Super resolution GAN (SRGAN) [1] and Enhanced Super resolution GAN (ESRGAN) [2]. These methods would be compared to reach a better accuracy on solving LRFR problem. After increased the image resolution, the image would be recognized using FaceNet. This research concluded that using super resolution as the preprocessing step for LRFR problem has achieved a higher accuracy compared to [3]. The highest accuracy achieved by using ESRGAN as the preprocessing and FaceNet for face recognition with accuracy of 98.96 % and Validation rate 96.757 %.

scholarly journals Low Resolution Face Recognition Using Generative Adversarial Network (Gan)

2020 ◽  
Author(s):  
Howard Martin ◽  
Suharjito
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Fine Tuning ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Resolution Image ◽  
The Past ◽  
Adversarial Network ◽  
Face Recognition System ◽  
Sr Method

Abstract Although face recognition system has achieved a very good performance in the past years, but Low Resolution Face Recognition (LRFR) is still challenging because low resolution image would decrease the accuracy. This research aimed to solved and get the best SR method to solved LRFR problem. YTF dataset used for fine tuning SR methods. While LFW dataset used for fine tuning and evaluating FaceNet model. The images would be increased using Res-Net GAN and RRDB GAN. Then the images would be recognized using FaceNet. The images that had been increased by RRDB GAN reached the highest accuracy 98.96 %.

Low-Resolution Face Recognition with Single Sample per Person via Domain Adaptation

2019 ◽  
Vol 33 (05) ◽  
pp. 1956005 ◽  
Author(s):  
Yongjie Chu ◽  
Yong Zhao ◽  
Touqeer Ahmad ◽  
Lindu Zhao
Keyword(s):  
Face Recognition ◽  
Domain Adaptation ◽  
Face Image ◽  
Single Sample ◽  
Low Resolution ◽  
Target Domain ◽  
Discriminative Ability ◽  
Surveillance Camera ◽  
Single Sample Per Person ◽  
Suspect Identification

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.

scholarly journals Shuffle block SRGAN for face image super-resolution reconstruction

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1429-1439
Author(s):  
Ziwei Zhang ◽  
Yangjing Shi ◽  
Xiaoshi Zhou ◽  
Hongfei Kan ◽  
Juan Wen
Keyword(s):  
Face Recognition ◽  
Learning Strategy ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Super Resolution ◽  
Face Image ◽  
Low Resolution ◽  
Index Method ◽  
Face Features ◽  
Image Super Resolution

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.

Low-resolution and open-set face recognition via recursive label propagation based on statistical classification

2019 ◽  
Vol 17 (02) ◽  
pp. 1940002 ◽  
Author(s):  
Shan Xue ◽  
Hong Zhu
Keyword(s):  
Face Recognition ◽  
Super Resolution ◽  
Recall Rate ◽  
Face Image ◽  
Label Propagation ◽  
Statistical Classification ◽  
Low Resolution ◽  
Face Images ◽  
Open Set ◽  
Discriminative Feature

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.

scholarly journals Upsampling Real-Time, Low-Resolution CCTV Videos Using Generative Adversarial Networks

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1312
Author(s):  
Debapriya Hazra ◽  
Yung-Cheol Byun
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Super Resolution ◽  
Poor Quality ◽  
Generative Adversarial Networks ◽  
Temporal Consistency ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Benchmark Datasets

Video super-resolution has become an emerging topic in the field of machine learning. The generative adversarial network is a framework that is widely used to develop solutions for low-resolution videos. Video surveillance using closed-circuit television (CCTV) is significant in every field, all over the world. A common problem with CCTV videos is sudden video loss or poor quality. In this paper, we propose a generative adversarial network that implements spatio-temporal generators and discriminators to enhance real-time low-resolution CCTV videos to high-resolution. The proposed model considers both foreground and background motion of a CCTV video and effectively models the spatial and temporal consistency from low-resolution video frames to generate high-resolution videos. Quantitative and qualitative experiments on benchmark datasets, including Kinetics-700, UCF101, HMDB51 and IITH_Helmet2, showed that our model outperforms the existing GAN models for video super-resolution.

THE RECONSTRUCTION OF SATELLITE IMAGES BASED ON FRACTAL INTERPOLATION

Fractals ◽  
2011 ◽  
Vol 19 (03) ◽  
pp. 347-354 ◽  
Author(s):  
CHING-JU CHEN ◽  
SHU-CHEN CHENG ◽  
Y. M. HUANG
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Satellite Image ◽  
Image Data ◽  
Image Resolution ◽  
Source Image ◽  
Fractal Interpolation ◽  
Low Resolution ◽  
Resolution Image ◽  
High Resolution Image

This study discussed the application of a fractal interpolation method in satellite image data reconstruction. It used low-resolution images as the source data for fractal interpolation reconstruction. Using this approach, a high-resolution image can be reconstructed when there is only a low-resolution source image available. The results showed that the high-resolution image data from fractal interpolation can effectively enhance the sharpness of the border contours. Implementing fractal interpolation on an insufficient image resolution image can avoid jagged edges and mosaic when enlarging the image, as well as improve the visibility of object features in the region of interest. The proposed approach can thus be a useful tool in land classification by satellite images.

scholarly journals Super-Resolution Enhancement Method Based on Generative Adversarial Network for Integral Imaging Microscopy

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2164
Author(s):  
Md. Shahinur Alam ◽  
Ki-Chul Kwon ◽  
Munkh-Uchral Erdenebat ◽  
Mohammed Y. Abbass ◽  
Md. Ashraful Alam ◽  
...  
Keyword(s):  
High Resolution ◽  
Resolution Enhancement ◽  
Three Dimensional ◽  
Super Resolution ◽  
Input Image ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Microscopic Object ◽  
Integral Imaging ◽  
Adversarial Network

The integral imaging microscopy system provides a three-dimensional visualization of a microscopic object. However, it has a low-resolution problem due to the fundamental limitation of the F-number (the aperture stops) by using micro lens array (MLA) and a poor illumination environment. In this paper, a generative adversarial network (GAN)-based super-resolution algorithm is proposed to enhance the resolution where the directional view image is directly fed as input. In a GAN network, the generator regresses the high-resolution output from the low-resolution input image, whereas the discriminator distinguishes between the original and generated image. In the generator part, we use consecutive residual blocks with the content loss to retrieve the photo-realistic original image. It can restore the edges and enhance the resolution by ×2, ×4, and even ×8 times without seriously hampering the image quality. The model is tested with a variety of low-resolution microscopic sample images and successfully generates high-resolution directional view images with better illumination. The quantitative analysis shows that the proposed model performs better for microscopic images than the existing algorithms.

scholarly journals Extreme Low-Resolution Activity Recognition Using a Super-Resolution-Oriented Generative Adversarial Network

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 670
Author(s):  
Mingzheng Hou ◽  
Song Liu ◽  
Jiliu Zhou ◽  
Yi Zhang ◽  
Ziliang Feng
Keyword(s):  
Activity Recognition ◽  
Large Scale ◽  
Super Resolution ◽  
General Activity ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Video Clips ◽  
Adversarial Network ◽  
Efficient Recognition ◽  
Low Resolution Images

Activity recognition is a fundamental and crucial task in computer vision. Impressive results have been achieved for activity recognition in high-resolution videos, but for extreme low-resolution videos, which capture the action information at a distance and are vital for preserving privacy, the performance of activity recognition algorithms is far from satisfactory. The reason is that extreme low-resolution (e.g., 12 × 16 pixels) images lack adequate scene and appearance information, which is needed for efficient recognition. To address this problem, we propose a super-resolution-driven generative adversarial network for activity recognition. To fully take advantage of the latent information in low-resolution images, a powerful network module is employed to super-resolve the extremely low-resolution images with a large scale factor. Then, a general activity recognition network is applied to analyze the super-resolved video clips. Extensive experiments on two public benchmarks were conducted to evaluate the effectiveness of our proposed method. The results demonstrate that our method outperforms several state-of-the-art low-resolution activity recognition approaches.

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