Asymmetric Pyramid Based Super Resolution from Very Low Resolution Face Image

2019 ◽  
pp. 694-702
Author(s):  
Xuebo Wang ◽  
Yao Lu ◽  
Xiaozhen Chen ◽  
Weiqi Li ◽  
Zijian Wang
Keyword(s):  
Super Resolution ◽  
Face Image ◽  
Low Resolution

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 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 %.

Research on Very Low Resolution Face Image Reconstruction

2014 ◽  
Vol 687-691 ◽  
pp. 3747-3750
Author(s):  
Zhi Zhuang Guo ◽  
Xiao Ling Wang
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Super Resolution ◽  
Face Image ◽  
Image Space ◽  
Low Resolution ◽  
Long Distance ◽  
The Face ◽  
The Relationship ◽  
New Algorithms

The resolution of the face image in video may lower than 16*16 in environmental such as ultra long distance, poor illumination and so on, with the very low resolution (VLR) face image the existing face super-resolution (SR) methods do not perform well. In this paper, we proposes a new algorithms by learning the relationship between high-resolution (HR) image space and the VLR image space for face SR.A new constrain, new data constrain are design for reconstruct HR face image form VLR face image. The Experiment results show that the proposed method can recover a clear face image from the VLR face image.

RBPNET: An asymptotic Residual Back-Projection Network for super-resolution of very low-resolution face image

2020 ◽  
Vol 376 ◽  
pp. 119-127
Author(s):  
Xiaozhen Chen ◽  
Xuebo Wang ◽  
Yao Lu ◽  
Weiqi Li ◽  
Zijian Wang ◽  
...  
Keyword(s):  
Super Resolution ◽  
Face Image ◽  
Back Projection ◽  
Low Resolution ◽  
Projection Network

scholarly journals Residual Attribute Attention Network for Face Image Super-Resolution

2019 ◽  
Vol 33 ◽  
pp. 9054-9061 ◽  
Author(s):  
Jingwei Xin ◽  
Nannan Wang ◽  
Xinbo Gao ◽  
Jie Li
Keyword(s):  
Feature Fusion ◽  
Super Resolution ◽  
Face Image ◽  
Great Success ◽  
Low Resolution ◽  
Shape Information ◽  
Attention Network ◽  
Semantic Level ◽  
Benchmark Database ◽  
Image Super Resolution

Facial prior knowledge based methods recently achieved great success on the task of face image super-resolution (SR). The combination of different type of facial knowledge could be leveraged for better super-resolving face images, e.g., facial attribute information with texture and shape information. In this paper, we present a novel deep end-to-end network for face super resolution, named Residual Attribute Attention Network (RAAN), which realizes the efficient feature fusion of various types of facial information. Specifically, we construct a multi-block cascaded structure network with dense connection. Each block has three branches: Texture Prediction Network (TPN), Shape Generation Network (SGN) and Attribute Analysis Network (AAN). We divide the task of face image reconstruction into three steps: extracting the pixel level representation information from the input very low resolution (LR) image via TPN and SGN, extracting the semantic level representation information by AAN from the input, and finally combining the pixel level and semantic level information to recover the high resolution (HR) image. Experiments on benchmark database illustrate that RAAN significantly outperforms state-of-the-arts for very low-resolution face SR problem, both quantitatively and qualitatively.

scholarly journals Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1956
Author(s):  
Jingyu Cong ◽  
Xianpeng Wang ◽  
Xiang Lan ◽  
Mengxing Huang ◽  
Liangtian Wan
Keyword(s):  
Neural Network ◽  
Continuous Wave ◽  
Super Resolution ◽  
Mimo Radar ◽  
Target Localization ◽  
Estimation Accuracy ◽  
Low Resolution ◽  
Range Estimation ◽  
Resolution Image ◽  
Resolution Imaging

The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.

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