On Matching Visible to Passive Infrared Face Images Using Image Synthesis & Denoising

2017 ◽  
Author(s):  
Nnamdi Osia ◽  
Thirimachos Bourlai
Keyword(s):  
Image Synthesis ◽  
Face Images ◽  
Passive Infrared

scholarly journals Style-Based Unsupervised Learning for Real-World Face Image Super-Resolution

2020 ◽  
Author(s):  
Ahmed Cheikh Sidiya ◽  
Xin Li
Keyword(s):  
Unsupervised Learning ◽  
Real World ◽  
Super Resolution ◽  
Image Synthesis ◽  
Face Image ◽  
Quality Improvements ◽  
Training Strategy ◽  
Face Images ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Face image synthesis has advanced rapidly in recent years. However, similar success has not been witnessed in related areas such as face single image super-resolution (SISR). The performance of SISR on real-world low-quality face images remains unsatisfactory. In this paper, we demonstrate how to advance the state-of-the-art in face SISR by leveraging style-based generator in unsupervised settings. For real-world low-resolution (LR) face images, we propose a novel unsupervised learning approach by combining style-based generator with relativistic discriminator. With a carefully designed training strategy, we demonstrate our converges faster and better suppresses artifacts than Bulat’s approach. When trained on an ensemble of high-quality datasets (CelebA, AFLW, LS3D-W, and VGGFace2), we report significant visual quality improvements over other competing methods especially for real-world low-quality face images such as those in Widerface. Additionally, we have verified that both our unsupervised approaches are capable of improving the matching performance of widely used face recognition systems such as OpenFace.

Fast Face Sketch–Photo Image Synthesis and Recognition

2016 ◽  
Vol 30 (10) ◽  
pp. 1656008 ◽  
Author(s):  
Zhenxue Chen ◽  
Kaifang Wang ◽  
Chengyun Liu
Keyword(s):  
Image Synthesis ◽  
Face Image ◽  
Effective Characteristics ◽  
Recognition Time ◽  
Sketch Recognition ◽  
Matching Effect ◽  
Face Images ◽  
Face Sketch Recognition ◽  
The Difference ◽  
Photo Image

Face sketch recognition has great practical value in the criminal detection, security and other fields. Especially, it can help the police narrow down potential suspects in criminal detection effectively. Face sketch represents the original photos in a simple and recognizable form, so sketch and photo are images of two different modes. In order to identify the corresponding sketch face image in a lot of photo face images, this paper presents an improved sketch–photo transformation algorithm, and it uses the effective characteristics of the photo image more reasonably during transforming a photo image into sketch. In this way, it can reduce the difference between the sketch and photo image to improve the matching effect, and save the recognition time. Many experiments on CUHK Face Sketch database including 188 sketch–photos prove the effectiveness of the method in this paper.

scholarly journals Generating Images of Face Poses for Pose Varying Face Recognition

2020 ◽  
Vol 9 (2) ◽  
pp. 351-356
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Representation Learning ◽  
Image Synthesis ◽  
Face Image ◽  
Learning Systems ◽  
Face Expression ◽  
Face Images ◽  
The Face

Deep learning has attracted several researchers in the field of computer vision due to its ability to perform face and object recognition tasks with high accuracy than the traditional shallow learning systems. The convolutional layers present in the deep learning systems help to successfully capture the distinctive features of the face. For biometric authentication, face recognition (FR) has been preferred due to its passive nature. Processing face images are accompanied by a series of complexities, like variation of pose, light, face expression, and make up. Although all aspects are important, the one that impacts the most face-related computer vision applications is pose. In face recognition, it has been long desired to have a method capable of bringing faces to the same pose, usually a frontal view, in order to ease recognition. Synthesizing different views of a face is still a great challenge, mostly because in nonfrontal face images there are loss of information when one side of the face occludes the other. Most solutions for FR fail to perform well in cases involving extreme pose variations as in such scenarios, the convolutional layers of the deep models are unable to find discriminative parts of the face for extracting information. Most of the architectures proposed earlier deal with the scenarios where the face images used for training as well as testing the deep learning models are frontal and nearfrontal. On the contrary, here a limited number of face images at different poses is used to train the model, where a number of separate generator models learn to map a single face image at any arbitrary pose to specific poses and the discriminator performs the task of face recognition along with discriminating a synthetic face from a realworld sample. To this end, this paper proposes a representation learning by rotating the face. Here an encoderdecoder structure of the generator enables to learn a representation that is both generative and discriminative, which can be used for face image synthesis and pose-invariant face recognition. This representation is explicitly disentangled from other face variations such as pose, through the pose code provided to the decoder and pose estimation in the discriminator.

How re-training process affect the performance of no-reference image quality metric for face images

2019 ◽  
Vol 2019 (5) ◽  
pp. 528-1-528-6
Author(s):  
Xinwei Liu ◽  
Christophe Charrier ◽  
Marius Pedersen ◽  
Patrick Bours
Keyword(s):  
Image Quality ◽  
Reference Image ◽  
Training Process ◽  
Quality Metric ◽  
Face Images ◽  
Image Quality Metric

Gradient Texture Classification Based On Age Prediction of Face Images

2014 ◽  
Vol 1 (3) ◽  
pp. 23-31
Author(s):  
Basava Raju ◽  
◽  
K. Y. Rama Devi ◽  
P. V. Kumar ◽  
◽  
...  
Keyword(s):  
Texture Classification ◽  
Face Images ◽  
Age Prediction

Image Synthesis by combining the Wavelets and Inverse Approaches

1999 ◽  
Vol 19 (Supplement1) ◽  
pp. 87-90
Author(s):  
D. SEKIJIMA ◽  
S. HAYANO ◽  
Y. SAITO ◽  
T.L. KUNII
Keyword(s):  
Image Synthesis
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