scholarly journals Enhanced Cycle Generative Adversarial Network for Generating Face Images of Untrained Races and Ages for Age Estimation

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yu Hwan Kim ◽  
Se Hyun Nam ◽  
Kang Ryoung Park
Keyword(s):  
Age Estimation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Face Images

Towards Accuracy Enhancement of Age Group Classification Using Generative Adversarial Networks

2021 ◽  
pp. 1-17
Author(s):  
Khaled ELKarazle ◽  
Valliappan Raman ◽  
Patrick Then
Keyword(s):  
Age Estimation ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Original Dataset ◽  
Age Group Classification ◽  
Facial Images

Age estimation models can be employed in many applications, including soft biometrics, content access control, targeted advertising, and many more. However, as some facial images are taken in unrestrained conditions, the quality relegates, which results in the loss of several essential ageing features. This study investigates how introducing a new layer of data processing based on a super-resolution generative adversarial network (SRGAN) model can influence the accuracy of age estimation by enhancing the quality of both the training and testing samples. Additionally, we introduce a novel convolutional neural network (CNN) classifier to distinguish between several age classes. We train one of our classifiers on a reconstructed version of the original dataset and compare its performance with an identical classifier trained on the original version of the same dataset. Our findings reveal that the classifier which trains on the reconstructed dataset produces better classification accuracy, opening the door for more research into building data-centric machine learning systems.

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.

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.

scholarly journals Facial Inpainting Using Generative Adversarial Network with Feature Reconstruction and Landmark Loss to Preserve Spatial Consistency in Unaligned Face Images

2020 ◽  
Vol 13 (6) ◽  
pp. 219-228
Author(s):  
Avin Maulana ◽  
◽  
Chastine Fatichah ◽  
Nanik Suciati ◽  
◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Perceptual Quality ◽  
Average Score ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Face Images ◽  
Spatial Consistency ◽  
Improvement Method ◽  
Feature Reconstruction

Facial inpainting is a process to reconstruct some missing or damaged pixels in the facial image. The reconstructed pixels should still be realistic, so the observer could not differentiate between the reconstructed pixels and the original one. However, there are a few problems that may arise when the inpainting algorithm has been done. There was an inconsistency between adjacent pixels when done on an unaligned face image, which caused a failure to reconstruct. We propose an improvement method in facial inpainting using Generative Adversarial Network (GAN) with additional loss using pre-trained network VGG-Net and face landmark. The feature reconstruction loss will help to preserve deep-feature on an image, while the landmark will increase the result’s perceptual quality. The training process has been done using a curriculum learning scenario. Qualitative results show that our inpainting method can reconstruct the missing area on unaligned face images. From the quantitative results, our proposed method achieves the average score of 21.528 and 0.665, while the maximum score of 29.922 and 0.908 on PSNR (Peak Signal to Noise Ratio) and SSIM (Structure Similarity Index Measure) metrics, respectively.

scholarly journals Semi-Supervised FaceGAN for Face-Age Progression and Regression with Synthesized Paired Images

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 603
Author(s):  
Quang T. M. Pham ◽  
Janghoon Yang ◽  
Jitae Shin
Keyword(s):  
Real Data ◽  
Similarity Score ◽  
Generative Adversarial Network ◽  
Age Progression ◽  
Regression Methods ◽  
Adversarial Network ◽  
Face Images ◽  
The Face ◽  
Quantitative Results ◽  
Human Faces

The performance of existing face age progression or regression methods is often limited by the lack of sufficient data to train the model. To deal with this problem, we introduce a novel framework that exploits synthesized images to improve the performance. A conditional generative adversarial network (GAN) is first developed to generate facial images with targeted ages. The semi-supervised GAN, called SS-FaceGAN, is proposed. This approach considers synthesized images with a target age and the face images from the real data so that age and identity features can be explicitly utilized in the objective function of the network. We analyze the performance of our method over previous studies qualitatively and quantitatively. The experimental results show that the SS-FaceGAN model can produce realistic human faces in terms of both identity preservation and age preservation with the quantitative results of a decent face detection rate of 97% and similarity score of 0.30 on average.

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 Hyperrealistic neural decoding: Reconstruction of face stimuli from fMRI measurements via the GAN latent space

2020 ◽  
Author(s):  
Thirza Dado ◽  
Yağmur Güçlütürk ◽  
Luca Ambrogioni ◽  
Gabriëlle Ras ◽  
Sander E. Bosch ◽  
...  
Keyword(s):  
Brain Activity ◽  
Generative Adversarial Networks ◽  
Neural Decoding ◽  
Imaging Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Face Images ◽  
Neural Information ◽  
Latent Representations ◽  
Brain Data

AbstractWe introduce a new framework for hyperrealistic reconstruction of perceived naturalistic stimuli from brain recordings. To this end, we embrace the use of generative adversarial networks (GANs) at the earliest step of our neural decoding pipeline by acquiring functional magnetic resonance imaging data as subjects perceived face images created by the generator network of a GAN. Subsequently, we used a decoding approach to predict the latent state of the GAN from brain data. Hence, latent representations for stimulus (re-)generation are obtained, leading to state-of-the-art image reconstructions. Altogether, we have developed a highly promising approach for decoding sensory perception from brain activity and systematically analyzing neural information processing in the human brain.DisclaimerThis manuscript contains no real face images; all faces are artificially generated by a generative adversarial network.

scholarly journals LAE-GAN-Based Face Image Restoration for Low-Light Age Estimation

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2329
Author(s):  
Se Hyun Nam ◽  
Yu Hwan Kim ◽  
Jiho Choi ◽  
Seung Baek Hong ◽  
Muhammad Owais ◽  
...  
Keyword(s):  
Age Estimation ◽  
State Of The Art ◽  
Estimation Method ◽  
Face Image ◽  
Low Light ◽  
Generative Adversarial Network ◽  
Estimation Performance ◽  
Adversarial Network ◽  
High Illumination ◽  
Facial Images

Age estimation is applicable in various fields, and among them, research on age estimation using human facial images, which are the easiest to acquire, is being actively conducted. Since the emergence of deep learning, studies on age estimation using various types of convolutional neural networks (CNN) have been conducted, and they have resulted in good performances, as clear images with high illumination were typically used in these studies. However, human facial images are typically captured in low-light environments. Age information can be lost in facial images captured in low-illumination environments, where noise and blur generated by the camera in the captured image reduce the age estimation performance. No study has yet been conducted on age estimation using facial images captured under low light. In order to overcome this problem, this study proposes a new generative adversarial network for low-light age estimation (LAE-GAN), which compensates for the brightness of human facial images captured in low-light environments, and a CNN-based age estimation method in which compensated images are input. When the experiment was conducted using the MORPH, AFAD, and FG-NET databases—which are open databases—the proposed method exhibited more accurate age estimation performance and brightness compensation in low-light images compared to state-of-the-art methods.

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