scholarly journals Multimodal Human Recognition in Significantly Low Illumination Environment Using Modified EnlightenGAN

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1934
Author(s):  
Ja Hyung Koo ◽  
Se Woon Cho ◽  
Na Rae Baek ◽  
Kang Ryoung Park
Keyword(s):  
Identification Accuracy ◽  
Indoor Environments ◽  
Generative Adversarial Network ◽  
Score Level Fusion ◽  
Human Recognition ◽  
Adversarial Network ◽  
The Face ◽  
Low Illumination ◽  
Camera Sensors ◽  
Better Than

Human recognition in indoor environments occurs both during the day and at night. During the day, human recognition encounters performance degradation owing to a blur generated when a camera captures a person’s image. However, when images are captured at night with a camera, it is difficult to obtain perfect images of a person without light, and the input images are very noisy owing to the properties of camera sensors in low-illumination environments. Studies have been conducted in the past on face recognition in low-illumination environments; however, there is lack of research on face- and body-based human recognition in very low illumination environments. To solve these problems, this study proposes a modified enlighten generative adversarial network (modified EnlightenGAN) in which a very low illumination image is converted to a normal illumination image, and the matching scores of deep convolutional neural network (CNN) features of the face and body in the converted image are combined with a score-level fusion for recognition. The two types of databases used in this study are the Dongguk face and body database version 3 (DFB-DB3) and the ChokePoint open dataset. The results of the experiment conducted using the two databases show that the human verification accuracy (equal error rate (ERR)) and identification accuracy (rank 1 genuine acceptance rate (GAR)) of the proposed method were 7.291% and 92.67% for DFB-DB3 and 10.59% and 87.78% for the ChokePoint dataset, respectively. Accordingly, the performance of the proposed method was better than the previous methods.

scholarly journals Face and Body-Based Human Recognition by GAN-Based Blur Restoration

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5229
Author(s):  
Ja Hyung Koo ◽  
Se Woon Cho ◽  
Na Rae Baek ◽  
Kang Ryoung Park
Keyword(s):  
Face Recognition ◽  
Motion Blur ◽  
Indoor Environments ◽  
Body Images ◽  
Long Distance ◽  
Generative Adversarial Network ◽  
Human Recognition ◽  
Adversarial Network ◽  
Face Images ◽  
Matching Score

The long-distance recognition methods in indoor environments are commonly divided into two categories, namely face recognition and face and body recognition. Cameras are typically installed on ceilings for face recognition. Hence, it is difficult to obtain a front image of an individual. Therefore, in many studies, the face and body information of an individual are combined. However, the distance between the camera and an individual is closer in indoor environments than that in outdoor environments. Therefore, face information is distorted due to motion blur. Several studies have examined deblurring of face images. However, there is a paucity of studies on deblurring of body images. To tackle the blur problem, a recognition method is proposed wherein the blur of body and face images is restored using a generative adversarial network (GAN), and the features of face and body obtained using a deep convolutional neural network (CNN) are used to fuse the matching score. The database developed by us, Dongguk face and body dataset version 2 (DFB-DB2) and ChokePoint dataset, which is an open dataset, were used in this study. The equal error rate (EER) of human recognition in DFB-DB2 and ChokePoint dataset was 7.694% and 5.069%, respectively. The proposed method exhibited better results than the state-of-art methods.

scholarly journals Generative Adversarial Network Based Rogue Device Identification Using Differential Constellation Trace Figure

2020 ◽  
Author(s):  
Zekun Chen ◽  
Linning Peng ◽  
Aiqun Hu ◽  
Hua Fu
Keyword(s):  
Prior Information ◽  
Learning Algorithm ◽  
Identification Accuracy ◽  
Generation Process ◽  
Classification Problems ◽  
Generative Adversarial Network ◽  
Device Identification ◽  
Security Issues ◽  
Adversarial Network ◽  
Rf Fingerprint

Abstract With the dramatic development of the internet of things (IoT), security issues such as identity authentication have received serious attention. The radio frequency (RF) fingerprint of IoT device is an inherent feature, which can hardly be imitated. In this paper, we propose a rogue device identification technique via RF fingerprinting using deep learning-based generative adversarial network (GAN). Being different from traditional classification problems in RF fingerprint identifications, this work focuses on unknown accessing device recognition without prior information. A differential constellation trace figure (DCTF) generation process is initially employed to transform RF fingerprint features from time-domain waveforms to 2-dimensional (2D) figures. Then, by using GAN, which is a kind of unsupervised learning algorithm, we can discriminate rogue devices without any prior information. An experimental verification system is built with 54 ZigBee devices regarded as recognized devices and accessing devices. A USRP receiver is used to capture the signal and identify the accessing devices. Experimental results show that the proposed rogue device identification method can achieve 95% identification accuracy in a real environment.

scholarly journals Semi-supervised generative adversarial networks for anomaly detection

2022 ◽  
Vol 132 ◽  
pp. 01016
Author(s):  
Juan Montenegro ◽  
Yeojin Chung
Keyword(s):  
Anomaly Detection ◽  
Video Surveillance ◽  
Daily Life ◽  
Generative Adversarial Networks ◽  
Gamma Correction ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Different Types ◽  
Better Than

Advancements in security have provided ways of recording anomalies of daily life through video surveillance. For the present investigation, a semi-supervised generative adversarial network model to detect and classify different types of crimes on videos. Additionally, we intend to tackle one of the most recurring difficulties of anomaly detection: illumination. For this, we propose a light augmentation algorithm based on gamma correction to help the semi-supervised generative adversarial networks on its classification task. The proposed process performs slightly better than other proposed models.

Inverse metacluster design using generative modeling for minimal scattering response

2021 ◽  
Vol 263 (1) ◽  
pp. 4990-4999
Author(s):  
Peter Lai ◽  
Feruza Amirkulova
Keyword(s):  
The Novel ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Generative Modeling ◽  
Recognition Ability ◽  
Total Scattering Cross Section ◽  
Scattering Response ◽  
Incident Waves ◽  
Optimal Configurations ◽  
Better Than

Metamaterials are subwavelength-sized artificial structures with the ability to manipulate incident waves in such a way that affects how the energy propagates throughout the medium. In acoustics, particularly placed scattering elements can reduce the total scattering cross section (TSCS) response. We propose a method to inversely design acoustic metamaterial configurations using deep learning and generative modeling. Using our proprietary multiple scattering solver with MATLAB optimization toolbox, we generate a dataset of optimal configurations with minimized TSCS within a discrete range of wavenumbers. We use this dataset to train a Conditional Wasserstein Generative Adversarial Network (cWGAN) to generate similar metacluster designs corresponding to specified input TSCS. To improve the coordinate recognition ability of the cWGAN, we include the novel CoordConv layer in the generator and critic. After training, the cWGAN can produce a variety of optimal configurations given an expected TSCS. Evaluating TSCS of generated configurations shows that the model is capable of proposing scatterer configurations that are comparable or better than the dataset within the optimized range.

scholarly journals LighterGAN: An Illumination Enhancement Method for Urban UAV Imagery

2021 ◽  
Vol 13 (7) ◽  
pp. 1371
Author(s):  
Junshu Wang ◽  
Yue Yang ◽  
Yuan Chen ◽  
Yuxing Han
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Original Structure ◽  
Edge Image ◽  
Generative Adversarial Network ◽  
Test Set ◽  
Generalization Ability ◽  
Adversarial Network ◽  
Enhancement Method ◽  
Low Illumination

In unmanned aerial vehicle based urban observation and monitoring, the performance of computer vision algorithms is inevitably limited by the low illumination and light pollution caused degradation, therefore, the application image enhancement is a considerable prerequisite for the performance of subsequent image processing algorithms. Therefore, we proposed a deep learning and generative adversarial network based model for UAV low illumination image enhancement, named LighterGAN. The design of LighterGAN refers to the CycleGAN model with two improvements—attention mechanism and semantic consistency loss—having been proposed to the original structure. Additionally, an unpaired dataset that was captured by urban UAV aerial photography has been used to train this unsupervised learning model. Furthermore, in order to explore the advantages of the improvements, both the performance in the illumination enhancement task and the generalization ability improvement of LighterGAN were proven in the comparative experiments combining subjective and objective evaluations. In the experiments with five cutting edge image enhancement algorithms, in the test set, LighterGAN achieved the best results in both visual perception and PIQE (perception based image quality evaluator, a MATLAB build-in function, the lower the score, the higher the image quality) score of enhanced images, scores were 4.91 and 11.75 respectively, better than EnlightenGAN the state-of-the-art. In the enhancement of low illumination sub-dataset Y (containing 2000 images), LighterGAN also achieved the lowest PIQE score of 12.37, 2.85 points lower than second place. Moreover, compared with the CycleGAN, the improvement of generalization ability was also demonstrated. In the test set generated images, LighterGAN was 6.66 percent higher than CycleGAN in subjective authenticity assessment and 3.84 lower in PIQE score, meanwhile, in the whole dataset generated images, the PIQE score of LighterGAN is 11.67, 4.86 lower than CycleGAN.

Underwater acoustic target classification based on u-shaped network

2021 ◽  
Vol 263 (5) ◽  
pp. 1338-1345
Author(s):  
Xue Lingzhi ◽  
Zeng Xiangyang
Keyword(s):  
Network Architecture ◽  
Feature Maps ◽  
Generative Adversarial Network ◽  
Underwater Acoustic ◽  
Adversarial Network ◽  
Label Information ◽  
Acoustic Target ◽  
Feature Information ◽  
Underwater Acoustic Signal ◽  
Better Than

Target recognition is a key task and a difficult technique in underwater acoustic signal processing. One of the most challenging problem is that the label information of the underwater acoustic samples is scarce or missing. To solve the problem, this paper presents a local skip connection u-shaped architecture network(U-Net)based on the convolutional neural network(CNN).To this end, the network architecture is designed cleverly to generate a contracting path and an expansive path to achieve the extraction of different scale features. More importantly, a local skip connection mechanism is proposed to optimize classification rates by reusing former feature maps in contracting path. The experimental results of the measured dataset demonstrate the recognition accuracy of the model is better than that of deep belief network(DBN) and generative adversarial network(GAN) networks.Further research on three kinds of network by visualization method shows that the proposed network can learn more effective feature information with limited samples.

scholarly journals Semi-Supervised FaceGAN for Face-Age Progression and Regression with Synthesized Paired Images

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 603
Author(s):  
Quang T. M. Pham ◽  
Janghoon Yang ◽  
Jitae Shin
Keyword(s):  
Real Data ◽  
Similarity Score ◽  
Generative Adversarial Network ◽  
Age Progression ◽  
Regression Methods ◽  
Adversarial Network ◽  
Face Images ◽  
The Face ◽  
Quantitative Results ◽  
Human Faces

The performance of existing face age progression or regression methods is often limited by the lack of sufficient data to train the model. To deal with this problem, we introduce a novel framework that exploits synthesized images to improve the performance. A conditional generative adversarial network (GAN) is first developed to generate facial images with targeted ages. The semi-supervised GAN, called SS-FaceGAN, is proposed. This approach considers synthesized images with a target age and the face images from the real data so that age and identity features can be explicitly utilized in the objective function of the network. We analyze the performance of our method over previous studies qualitatively and quantitatively. The experimental results show that the SS-FaceGAN model can produce realistic human faces in terms of both identity preservation and age preservation with the quantitative results of a decent face detection rate of 97% and similarity score of 0.30 on average.

scholarly journals Generative Adversarial Network Based Rogue Device Identification Using Differential Constellation Trace Figure

2021 ◽  
Author(s):  
Zekun Chen ◽  
Linning Peng ◽  
Aiqun Hu ◽  
Hua Fu
Keyword(s):  
Prior Information ◽  
Learning Algorithm ◽  
Software Radio ◽  
Identification Accuracy ◽  
Generation Process ◽  
Classification Problems ◽  
Generative Adversarial Network ◽  
Device Identification ◽  
Adversarial Network ◽  
Rf Fingerprint

Abstract With the dramatic development of the internet of things (IoT), security issues such as identity authentication have received serious attention. The radio frequency (RF) fingerprint of IoT device is an inherent feature, which can hardly be imitated. In this paper, we propose a rogue device identification technique via RF fingerprinting using deep learning-based generative adversarial network (GAN). Being different from traditional classification problems in RF fingerprint identifications, this work focuses on unknown accessing device recognition without prior information. A differential constellation trace figure (DCTF) generation process is initially employed to transform RF fingerprint features from time-domain waveforms to 2-dimensional (2D) figures. Then, by using GAN, which is a kind of unsupervised learning algorithm, we can discriminate rogue devices without any prior information. An experimental verification system is built with 54 ZigBee devices regarded as recognized devices and accessing devices. A Universal Software Radio Peripheral (USRP) receiver is used to capture the signal and identify the accessing devices. Experimental results show that the proposed rogue device identification method can achieve 95%identification accuracy in a real environment.

scholarly journals Low-Dose CT Image Denoising with Improving WGAN and Hybrid Loss Function

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhihua Li ◽  
Weili Shi ◽  
Qiwei Xing ◽  
Yu Miao ◽  
Wei He ◽  
...  
Keyword(s):  
Loss Function ◽  
Low Dose ◽  
Structural Similarity ◽  
Image Method ◽  
Ct Image ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network ◽  
Quantitative Measurements ◽  
Better Than

The X-ray radiation from computed tomography (CT) brought us the potential risk. Simply decreasing the dose makes the CT images noisy and diagnostic performance compromised. Here, we develop a novel denoising low-dose CT image method. Our framework is based on an improved generative adversarial network coupling with the hybrid loss function, including the adversarial loss, perceptual loss, sharpness loss, and structural similarity loss. Among the loss function terms, perceptual loss and structural similarity loss are made use of to preserve textural details, and sharpness loss can make reconstruction images clear. The adversarial loss can sharp the boundary regions. The results of experiments show the proposed method can effectively remove noise and artifacts better than the state-of-the-art methods in the aspects of the visual effect, the quantitative measurements, and the texture details.

scholarly journals 3D-Aided Deep Pose-Invariant Face Recognition

2018 ◽  
Author(s):  
Jian Zhao ◽  
Lin Xiong ◽  
Yu Cheng ◽  
Yi Cheng ◽  
Jianshu Li ◽  
...  
Keyword(s):  
Face Recognition ◽  
Real Data ◽  
Training Data ◽  
Generative Adversarial Network ◽  
High Data ◽  
Adversarial Network ◽  
The Arts ◽  
The Face ◽  
In The Wild ◽  
Data Efficiency

Learning from synthetic faces, though perhaps appealing for high data efficiency, may not bring satisfactory performance due to the distribution discrepancy of the synthetic and real face images. To mitigate this gap, we propose a 3D-Aided Deep Pose-Invariant Face Recognition Model (3D-PIM), which automatically recovers realistic frontal faces from arbitrary poses through a 3D face model in a novel way. Specifically, 3D-PIM incorporates a simulator with the aid of a 3D Morphable Model (3D MM) to obtain shape and appearance prior for accelerating face normalization learning, requiring less training data. It further leverages a global-local Generative Adversarial Network (GAN) with multiple critical improvements as a refiner to enhance the realism of both global structures and local details of the face simulator’s output using unlabelled real data only, while preserving the identity information. Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks clearly demonstrate superiority of the proposed model over state-of-the-arts.

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