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 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 RoboCoDraw: Robotic Avatar Drawing with GAN-Based Style Transfer and Time-Efficient Path Optimization

2020 ◽  
Vol 34 (06) ◽  
pp. 10402-10409
Author(s):  
Tianying Wang ◽  
Wei Qi Toh ◽  
Hao Zhang ◽  
Xiuchao Sui ◽  
Shaohua Li ◽  
...  
Keyword(s):  
Face Image ◽  
Path Optimization ◽  
Robotic Arm ◽  
Human Face ◽  
Generative Adversarial Network ◽  
Style Transfer ◽  
Adversarial Network ◽  
Face Images ◽  
Drawing System ◽  
Collaborative Robot

Robotic drawing has become increasingly popular as an entertainment and interactive tool. In this paper we present RoboCoDraw, a real-time collaborative robot-based drawing system that draws stylized human face sketches interactively in front of human users, by using the Generative Adversarial Network (GAN)-based style transfer and a Random-Key Genetic Algorithm (RKGA)-based path optimization. The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm. A core component in this system is the AvatarGAN proposed by us, which generates a cartoon avatar face image from a real human face. AvatarGAN is trained with unpaired face and avatar images only and can generate avatar images of much better likeness with human face images in comparison with the vanilla CycleGAN. After the avatar image is generated, it is fed to a line extraction algorithm and converted to sketches. An RKGA-based path optimization algorithm is applied to find a time-efficient robotic drawing path to be executed by the robotic arm. We demonstrate the capability of RoboCoDraw on various face images using a lightweight, safe collaborative robot UR5.

Multi-Pose Face Recognition with Two-Cycle Generative Adversarial Network

2020 ◽  
Vol 40 (19) ◽  
pp. 1910002
Author(s):  
徐志京 Xu Zhijing ◽  
王东 Wang Dong
Keyword(s):  
Face Recognition ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Presentation Attack Face Image Generation Based on a Deep Generative Adversarial Network

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1810
Author(s):  
Dat Tien Nguyen ◽  
Tuyen Danh Pham ◽  
Ganbayar Batchuluun ◽  
Kyoung Jun Noh ◽  
Kang Ryoung Park
Keyword(s):  
Recognition Task ◽  
Recognition System ◽  
Attack Detection ◽  
Image Generation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Face Images ◽  
Problem Presentation ◽  
Recognition Systems ◽  
Public Datasets

Although face-based biometric recognition systems have been widely used in many applications, this type of recognition method is still vulnerable to presentation attacks, which use fake samples to deceive the recognition system. To overcome this problem, presentation attack detection (PAD) methods for face recognition systems (face-PAD), which aim to classify real and presentation attack face images before performing a recognition task, have been developed. However, the performance of PAD systems is limited and biased due to the lack of presentation attack images for training PAD systems. In this paper, we propose a method for artificially generating presentation attack face images by learning the characteristics of real and presentation attack images using a few captured images. As a result, our proposed method helps save time in collecting presentation attack samples for training PAD systems and possibly enhance the performance of PAD systems. Our study is the first attempt to generate PA face images for PAD system based on CycleGAN network, a deep-learning-based framework for image generation. In addition, we propose a new measurement method to evaluate the quality of generated PA images based on a face-PAD system. Through experiments with two public datasets (CASIA and Replay-mobile), we show that the generated face images can capture the characteristics of presentation attack images, making them usable as captured presentation attack samples for PAD system training.

Coupled generative adversarial network for heterogeneous face recognition

2020 ◽  
Vol 94 ◽  
pp. 103861 ◽  
Author(s):  
Seyed Mehdi Iranmanesh ◽  
Benjamin Riggan ◽  
Shuowen Hu ◽  
Nasser M. Nasrabadi
Keyword(s):  
Face Recognition ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Heterogeneous Face Recognition

scholarly journals Criminal Face Recognition Using GAN

2020 ◽  
Vol 5 (6) ◽  
pp. 1526-1528
Author(s):  
Anitta George ◽  
Krishnendu K A ◽  
Anusree K ◽  
Adira Suresh Nair ◽  
Hari Shree
Keyword(s):  
Face Recognition ◽  
Learning Approach ◽  
Security Measures ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Machine Learning Approach ◽  
Artificial Image ◽  
The Given ◽  
Biometric Feature

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.

scholarly journals Homomorphic Filtering and Phase-Based Matching for Cross-Spectral Cross-Distance Face Recognition

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4575
Author(s):  
Fitri Arnia ◽  
Maulisa Oktiana ◽  
Khairun Saddami ◽  
Khairul Munadi ◽  
Roslidar Roslidar ◽  
...  
Keyword(s):  
Face Recognition ◽  
Near Infrared ◽  
Recognition Performance ◽  
Extraction Process ◽  
Face Verification ◽  
Surveillance Systems ◽  
Long Distance ◽  
Face Images ◽  
Homomorphic Filtering ◽  
Band Limited

Facial recognition has a significant application for security, especially in surveillance technologies. In surveillance systems, recognizing faces captured far away from the camera under various lighting conditions, such as in the daytime and nighttime, is a challenging task. A system capable of recognizing face images in both daytime and nighttime and at various distances is called Cross-Spectral Cross Distance (CSCD) face recognition. In this paper, we proposed a phase-based CSCD face recognition approach. We employed Homomorphic filtering as photometric normalization and Band Limited Phase Only Correlation (BLPOC) for image matching. Different from the state-of-the-art methods, we directly utilized the phase component from an image, without the need for a feature extraction process. The experiment was conducted using the Long-Distance Heterogeneous Face Database (LDHF-DB). The proposed method was evaluated in three scenarios: (i) cross-spectral face verification at 1m, (ii) cross-spectral face verification at 60m, and (iii) cross-spectral face verification where the probe images (near-infrared (NIR) face images) were captured at 1m and the gallery data (face images) was captured at 60 m. The proposed CSCD method resulted in the best recognition performance among the CSCD baseline approaches, with an Equal Error Rate (EER) of 5.34% and a Genuine Acceptance Rate (GAR) of 93%.

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