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.

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 Remaining Useful Life Estimation Using Deep Convolutional Generative Adversarial Networks Based on an Autoencoder Scheme

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Guisheng Hou ◽  
Shuo Xu ◽  
Nan Zhou ◽  
Lei Yang ◽  
Quanhao Fu
Keyword(s):  
Feature Extraction ◽  
Short Term Memory ◽  
Health Management ◽  
Remaining Useful Life ◽  
Fine Tuning ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Useful Life ◽  
Prediction Approach

Accurate predictions of remaining useful life (RUL) of important components play a crucial role in system reliability, which is the basis of prognostics and health management (PHM). This paper proposed an integrated deep learning approach for RUL prediction of a turbofan engine by integrating an autoencoder (AE) with a deep convolutional generative adversarial network (DCGAN). In the pretraining stage, the reconstructed data of the AE not only participate in its error reconstruction but also take part in the DCGAN parameter training as the generated data of the DCGAN. Through double-error reconstructions, the capability of feature extraction is enhanced, and high-level abstract information is obtained. In the fine-tuning stage, a long short-term memory (LSTM) network is used to extract the sequential information from the features to predict the RUL. The effectiveness of the proposed scheme is verified on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. The superiority of the proposed method is demonstrated via excellent prediction performance and comparisons with other existing state-of-the-art prognostics. The results of this study suggest that the proposed data-driven prognostic method offers a new and promising prediction approach and an efficient feature extraction scheme.

scholarly journals Unified Generative Adversarial Networks for Multidomain Fingerprint Presentation Attack Detection

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1089
Author(s):  
Soha B. Sandouka ◽  
Yakoub Bazi ◽  
Haikel Alhichri ◽  
Naif Alajlan
Keyword(s):  
Attack Detection ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Source Domain ◽  
The Public ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Biometric Systems ◽  
Security And Reliability ◽  
Presentation Attack Detection

With the rapid growth of fingerprint-based biometric systems, it is essential to ensure the security and reliability of the deployed algorithms. Indeed, the security vulnerability of these systems has been widely recognized. Thus, it is critical to enhance the generalization ability of fingerprint presentation attack detection (PAD) cross-sensor and cross-material settings. In this work, we propose a novel solution for addressing the case of a single source domain (sensor) with large labeled real/fake fingerprint images and multiple target domains (sensors) with only few real images obtained from different sensors. Our aim is to build a model that leverages the limited sample issues in all target domains by transferring knowledge from the source domain. To this end, we train a unified generative adversarial network (UGAN) for multidomain conversion to learn several mappings between all domains. This allows us to generate additional synthetic images for the target domains from the source domain to reduce the distribution shift between fingerprint representations. Then, we train a scale compound network (EfficientNetV2) coupled with multiple head classifiers (one classifier for each domain) using the source domain and the translated images. The outputs of these classifiers are then aggregated using an additional fusion layer with learnable weights. In the experiments, we validate the proposed methodology on the public LivDet2015 dataset. The experimental results show that the proposed method improves the average classification accuracy over twelve classification scenarios from 67.80 to 80.44% after adaptation.

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 %.

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