TR-GAN: thermal to RGB face synthesis with generative adversarial network for cross-modal face recognition

2020 ◽  
Author(s):  
Landry Kezebou ◽  
Victor Oludare ◽  
Karen Panetta ◽  
Sos S. Agaian
Keyword(s):  
Face Recognition ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Face Synthesis

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

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 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 Identification of Lost Children using Face Aging with Conditional GAN

2021 ◽  
Vol 40 ◽  
pp. 03005
Author(s):  
Amisha Pupala ◽  
Samruddhi Mokal ◽  
Neha Pandit ◽  
Smita Bharne
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Generative Adversarial Network ◽  
The Public ◽  
Number Of Children ◽  
Adversarial Network ◽  
The Many ◽  
Face Aging ◽  
Lost Child

Face recognition technology is a big area that consists of the many features in it but it also comes with some of the factors which affect this technology, one of the factors is Face aging which makes face recognition more difficult. As in India, a large number of children go missing every year. Also just using a photograph is not enough for the process to proceed smoothly and it results in a huge percentage of the missing child cases remain untraced. This paper presents a novel use of face recognition with face aging to overcome the limitation of existing systems. The proposed system has a portal where the public can upload an image of a suspected child and also have a feature where searching for any lost child is possible. The proposed system has mainly concentrated on an Age Conditional generative adversarial network (C-GAN) algorithm for face aging and the FaceNet algorithm for face feature extraction and face recognition.

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.

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