scholarly journals Temporal face feature progression with cycle GAN

2022 ◽  
Vol 2161 (1) ◽  
pp. 012008
Author(s):  
Roy Ashish ◽  
B G Prasad
Keyword(s):  
Aging Process ◽  
The Elderly ◽  
Generative Adversarial Networks ◽  
Facial Structure ◽  
Facial Aging ◽  
Age Progression ◽  
Adversarial Networks ◽  
Face Features ◽  
Initial Appearance ◽  
Age Category

Abstract The aging process creates significant changes in the appearances of people’s faces. When compared to other causes of variation in face imaging, aging-related variation has specific distinct properties. Facial Aging variations, for example, is unique for each person; it occurs gradually and is significantly influenced by other characteristics including health, gender, and life-style. As a result, the proposed effort will use Generative Adversarial Networks to address these critical concerns (GANs). Generative Adversarial Networks (GAN’s) is made up of a generator and a discriminator network. The generator model generates images that a discriminator model analyses to determine if they are real or fake. This paper provides a Temporal Face Feature Progressive framework with Cycle GAN, which maintains the initial appearance and identity in the elderly aspect of their facial structure. To address aging concerns, our goal is to transform an initial age category image into a targeted age with age progression. We show that our temporal face features progressive cycle GAN learns and transfers facial traits from the source group to the targeted group by training various images. The IMDB-WIKI Face dataset has been used to obtain the results for the same.

Age Progression using Generative Adversarial Networks

2020 ◽  
Author(s):  
Pallavi Madhukar ◽  
Rachana Chetan ◽  
Supriya Prasad ◽  
Mohamed Shayan ◽  
B. Niranjana Krupa
Keyword(s):  
Generative Adversarial Networks ◽  
Age Progression ◽  
Adversarial Networks

scholarly journals Age Progression and Regression with Spatial Attention Modules

2020 ◽  
Vol 34 (07) ◽  
pp. 11378-11385
Author(s):  
Qi Li ◽  
Yunfan Liu ◽  
Zhenan Sun
Keyword(s):  
Spatial Attention ◽  
Generative Adversarial Networks ◽  
Age Progression ◽  
Training Images ◽  
Adversarial Networks ◽  
Face Images ◽  
Multiple Datasets ◽  
Image Translation ◽  
In The Wild ◽  
Face Aging

Age progression and regression refers to aesthetically rendering a given face image to present effects of face aging and rejuvenation, respectively. Although numerous studies have been conducted in this topic, there are two major problems: 1) multiple models are usually trained to simulate different age mappings, and 2) the photo-realism of generated face images is heavily influenced by the variation of training images in terms of pose, illumination, and background. To address these issues, in this paper, we propose a framework based on conditional Generative Adversarial Networks (cGANs) to achieve age progression and regression simultaneously. Particularly, since face aging and rejuvenation are largely different in terms of image translation patterns, we model these two processes using two separate generators, each dedicated to one age changing process. In addition, we exploit spatial attention mechanisms to limit image modifications to regions closely related to age changes, so that images with high visual fidelity could be synthesized for in-the-wild cases. Experiments on multiple datasets demonstrate the ability of our model in synthesizing lifelike face images at desired ages with personalized features well preserved, and keeping age-irrelevant regions unchanged.

scholarly journals WGAN-E: A Generative Adversarial Networks for Facial Feature Security

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 486
Author(s):  
Chunxue Wu ◽  
Bobo Ju ◽  
Yan Wu ◽  
Neal N. Xiong ◽  
Sheng Zhang
Keyword(s):  
Face Recognition ◽  
Intelligent Systems ◽  
Human Life ◽  
Facial Feature ◽  
Generative Adversarial Networks ◽  
Facial Features ◽  
Learning Technology ◽  
Adversarial Networks ◽  
Face Features ◽  
Hashing Algorithm

Artificial intelligence technology plays an increasingly important role in human life. For example, distinguishing different people is an essential capability of many intelligent systems. To achieve this, one possible technical means is to perceive and recognize people by optical imaging of faces, so-called face recognition technology. After decades of research and development, especially the emergence of deep learning technology in recent years, face recognition has made great progress with more and more applications in the fields of security, finance, education, social security, etc. The field of computer vision has become one of the most successful branch areas. With the wide application of biometrics technology, bio-encryption technology came into being. Aiming at the problems of classical hash algorithm and face hashing algorithm based on Multiscale Block Local Binary Pattern (MB-LBP) feature improvement, this paper proposes a method based on Generative Adversarial Networks (GAN) to encrypt face features. This work uses Wasserstein Generative Adversarial Networks Encryption (WGAN-E) to encrypt facial features. Because the encryption process is an irreversible one-way process, it protects facial features well. Compared with the traditional face hashing algorithm, the experimental results show that the face feature encryption algorithm has better confidentiality.

scholarly journals Enhancement of Multi-Target Tracking Performance via Image Restoration and Face Embedding in Dynamic Environments

2021 ◽  
Vol 11 (2) ◽  
pp. 649
Author(s):  
Ji Seong Kim ◽  
Doo Soo Chang ◽  
Yong Suk Choi
Keyword(s):  
Image Restoration ◽  
Autonomous Vehicles ◽  
Super Resolution ◽  
Dynamic Environments ◽  
Generative Adversarial Networks ◽  
Distance Measurements ◽  
Adversarial Networks ◽  
Face Features ◽  
Real People ◽  
Multi Target Tracking

In this paper, we propose several methods to improve the performance of multiple object tracking (MOT), especially for humans, in dynamic environments such as robots and autonomous vehicles. The first method is to restore and re-detect unreliable results to improve the detection. The second is to restore noisy regions in the image before the tracking association to improve the identification. To implement the image restoration function used in these two methods, an image inference model based on SRGAN (super-resolution generative adversarial networks) is used. Finally, the third method includes an association method using face features to reduce failures in the tracking association. Three distance measurements are designed so that this method can be applied to various environments. In order to validate the effectiveness of our proposed methods, we select two baseline trackers for comparative experiments and construct a robotic environment that interacts with real people and provides services. Experimental results demonstrate that the proposed methods efficiently overcome dynamic situations and show favorable performance in general situations.

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