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 FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 807 ◽  
Author(s):  
Weiwei Zhuang ◽  
Liang Chen ◽  
Chaoqun Hong ◽  
Yuxin Liang ◽  
Keshou Wu
Keyword(s):  
Face Recognition ◽  
Generative Adversarial Networks ◽  
Facial Features ◽  
Adversarial Networks ◽  
Key Points ◽  
In The Wild ◽  
Novel Method ◽  
Face Synthesis ◽  
Unconstrained Face

Face recognition has been comprehensively studied. However, face recognition in the wild still suffers from unconstrained face directions. Frontal face synthesis is a popular solution, but some facial features are missed after synthesis. This paper presents a novel method for pose-invariant face recognition. It is based on face transformation with key points alignment based on generative adversarial networks (FT-GAN). In this method, we introduce CycleGAN for pixel transformation to achieve coarse face transformation results, and these results are refined by key point alignment. In this way, frontal face synthesis is modeled as a two-task process. The results of comprehensive experiments show the effectiveness of FT-GAN.

HUMAN FACE RECOGNITION FROM A SINGLE FRONT VIEW

1992 ◽  
Vol 06 (04) ◽  
pp. 571-593 ◽  
Author(s):  
CHING-WEN CHEN ◽  
CHUNG-LIN HUANG
Keyword(s):  
Face Recognition ◽  
Active Contour Model ◽  
Facial Feature ◽  
Recognition System ◽  
Facial Features ◽  
Human Face Recognition ◽  
Front View ◽  
Deformable Template ◽  
Face Recognition System ◽  
Feature Values

This paper presents a face recognition system which can identify the unknown identity effectively using the front-view facial features. In front-view facial feature extractions, we can capture the contours of eyes and mouth by the deformable template model because of their analytically describable shapes. However, the shapes of eyebrows, nostrils and face are difficult to model using a deformable template. We extract them by using the active contour model (snake). After the contours of all facial features have been captured, we calculate effective feature values from these extracted contours and construct databases for unknown identities classification. In the database generation phase, 12 models are photographed, and feature vectors are calculated for each portrait. In the identification phase if any one of these 12 persons has his picture taken again, the system can recognize his identity.

scholarly journals Serlio and Artificial Intelligence: Problematizing the Image-to-Object Workflow

2021 ◽  
pp. 3-12
Author(s):  
Jean Jaminet ◽  
Gabriel Esquivel ◽  
Shane Bugni
Keyword(s):  
Artificial Intelligence ◽  
Data Storage ◽  
Intelligent Systems ◽  
Communication Theory ◽  
Human Perception ◽  
Generative Adversarial Networks ◽  
Virtual Design ◽  
Storage And Retrieval ◽  
Adversarial Networks ◽  
Design Production

AbstractVirtual design production demands that information be increasingly encoded and decoded with image compression technologies. Since the Renaissance, the discourses of language and drawing and their actuation by the classical disciplinary treatise have been fundamental to the production of knowledge within the building arts. These early forms of data compression provoke reflection on theory and technology as critical counterparts to perception and imagination unique to the discipline of architecture. This research examines the illustrated expositions of Sebastiano Serlio through the lens of artificial intelligence (AI). The mimetic powers of technological data storage and retrieval and Serlio’s coded operations of orthographic projection drawing disclose other aesthetic and formal logics for architecture and its image that exist outside human perception. Examination of aesthetic communication theory provides a conceptual dimension of how architecture and artificial intelligent systems integrate both analog and digital modes of information processing. Tools and methods are reconsidered to propose alternative AI workflows that complicate normative and predictable linear design processes. The operative model presented demonstrates how augmenting and interpreting layered generative adversarial networks drive an integrated parametric process of three-dimensionalization. Concluding remarks contemplate the role of human design agency within these emerging modes of creative digital production.

scholarly journals Synthesis of Normal Heart Sounds Using Generative Adversarial Networks and Empirical Wavelet Transform

2020 ◽  
Vol 10 (19) ◽  
pp. 7003 ◽  
Author(s):  
Pedro Narváez ◽  
Winston S. Percybrooks
Keyword(s):  
Wavelet Transform ◽  
Intelligent Systems ◽  
Heart Sound ◽  
State Of The Art ◽  
Computational Cost ◽  
Heart Sounds ◽  
Generative Adversarial Networks ◽  
Sound Classification ◽  
Adversarial Networks ◽  
Empirical Wavelet Transform

Currently, there are many works in the literature focused on the analysis of heart sounds, specifically on the development of intelligent systems for the classification of normal and abnormal heart sounds. However, the available heart sound databases are not yet large enough to train generalized machine learning models. Therefore, there is interest in the development of algorithms capable of generating heart sounds that could augment current databases. In this article, we propose a model based on generative adversary networks (GANs) to generate normal synthetic heart sounds. Additionally, a denoising algorithm is implemented using the empirical wavelet transform (EWT), allowing a decrease in the number of epochs and the computational cost that the GAN model requires. A distortion metric (mel–cepstral distortion) was used to objectively assess the quality of synthetic heart sounds. The proposed method was favorably compared with a mathematical model that is based on the morphology of the phonocardiography (PCG) signal published as the state of the art. Additionally, different heart sound classification models proposed as state-of-the-art were also used to test the performance of such models when the GAN-generated synthetic signals were used as test dataset. In this experiment, good accuracy results were obtained with most of the implemented models, suggesting that the GAN-generated sounds correctly capture the characteristics of natural heart sounds.

The Role of Eyebrows in Face Recognition

Perception ◽  
10.1068/p5027 ◽  
2003 ◽  
Vol 32 (3) ◽  
pp. 285-293 ◽  
Author(s):  
Javid Sadr ◽  
Izzat Jarudi ◽  
Pawan Sinha
Keyword(s):  
Face Recognition ◽  
Nonverbal Communication ◽  
Emotional Expression ◽  
Recognition Performance ◽  
Facial Feature ◽  
Facial Features ◽  
Facial Aesthetics ◽  
Fundamental Challenge ◽  
Recognition Systems

A fundamental challenge in face recognition lies in determining which facial characteristics are important in the identification of faces. Several studies have indicated the significance of certain facial features in this regard, particularly internal ones such as the eyes and mouth. Surprisingly, however, one rather prominent facial feature has received little attention in this domain: the eyebrows. Past work has examined the role of eyebrows in emotional expression and nonverbal communication, as well as in facial aesthetics and sexual dimorphism. However, it has not been made clear whether the eyebrows play an important role in the identification of faces. Here, we report experimental results which suggest that for face recognition the eyebrows may be at least as influential as the eyes. Specifically, we find that the absence of eyebrows in familiar faces leads to a very large and significant disruption in recognition performance. In fact, a significantly greater decrement in face recognition is observed in the absence of eyebrows than in the absence of eyes. These results may have important implications for our understanding of the mechanisms of face recognition in humans as well as for the development of artificial face-recognition systems.

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.

scholarly journals Understanding Trending Variants of Generative Adversarial Networks

2018 ◽  
Vol 7 (3.12) ◽  
pp. 864
Author(s):  
Tanvi Bhandarkar ◽  
A Murugan
Keyword(s):  
Intelligent Systems ◽  
Applied Field ◽  
Domain Adaptation ◽  
Generative Adversarial Networks ◽  
High Dimensional ◽  
Adversarial Networks ◽  
Academic Fields ◽  
Image Translation ◽  
Latent Space ◽  
Adversarial Models

Generative Adversarial Networks (GAN) have its major contribution to the field of Artificial Intelligence. It is becoming so powerful by paving its way in numerous applications of intelligent systems. This is primarily due to its astute prospect of learning and solving complex and high-dimensional problems from the latent space. With the growing demands of GANs, it is necessary to seek its potential and impact in implementations. In short span of time, it has witnessed several variants and extensions in image translation, domain-adaptation and other academic fields. This paper provides an understanding of such imperative GANs mutants and surveys the existing adversarial models which are prominent in their applied field. 

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