519 research outputs found
Facial Attribute Capsules for Noise Face Super Resolution
Existing face super-resolution (SR) methods mainly assume the input image to
be noise-free. Their performance degrades drastically when applied to
real-world scenarios where the input image is always contaminated by noise. In
this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with
the problem of high-scale super-resolution of noisy face image. Capsule is a
group of neurons whose activity vector models different properties of the same
entity. Inspired by the concept of capsule, we propose an integrated
representation model of facial information, which named Facial Attribute
Capsule (FAC). In the SR processing, we first generated a group of FACs from
the input LR face, and then reconstructed the HR face from this group of FACs.
Aiming to effectively improve the robustness of FAC to noise, we generate FAC
in semantic, probabilistic and facial attributes manners by means of integrated
learning strategy. Each FAC can be divided into two sub-capsules: Semantic
Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial
attribute in detail from two aspects of semantic representation and probability
distribution. The group of FACs model an image as a combination of facial
attribute information in the semantic space and probabilistic space by an
attribute-disentangling way. The diverse FACs could better combine the face
prior information to generate the face images with fine-grained semantic
attributes. Extensive benchmark experiments show that our method achieves
superior hallucination results and outperforms state-of-the-art for very low
resolution (LR) noise face image super resolution.Comment: To appear in AAAI 202
Deep Learning for Robust Super-resolution
Super Resolution (SR) is a process in which a high-resolution counterpart of an image is reconstructed from its low-resolution sample. Generative Adversarial Networks (GAN), known for their ability of hyper-realistic image generation, demonstrate promising results in performing SR task. High-scale SR, where the super-resolved image is notably larger than low-resolution input, is a challenging but very beneficial task. By employing an SR model, the data can be compressed, more details can be extracted from cheap sensors and cameras, and the noise level will be reduced dramatically. As a result, the high-scale SR model can contribute significantly to face-related tasks, such as identification, face detection, and surveillance systems. Moreover, the resolution of medical scans will be notably increased. So more details can be detected and the early-stage diagnosis will be possible for many diseases such as cancer. Moreover, cheaper and more available scanning devices can be used for accurate abnormality detection. As a result, more lives can be saved because of the enhancement of the accuracy and the availability of scans.
In this thesis, the first multi-scale gradient capsule GAN for SR is proposed. First, this model is trained on CelebA dataset for face SR. The performance of the proposed model is compared with state-of-the-art works and its supremacy in all similarity metrics is demonstrated. A new perceptual similarity index is introduced as well and the proposed architecture outperforms related works in this metric with a notable margin. A robustness test is conducted and the drop in similarity metrics is investigated. As a result, the proposed SR model is not only more accurate but also more robust than the state-of-the-art works.
Since the proposed model is considered as a general SR system, it is also employed for prostate MRI SR. Prostate cancer is a very common disease among adult men. One in seven Canadian men is diagnosed with this cancer in their lifetime. SR can facilitate early diagnosis and potentially save many lives. The proposed model is trained on the Prostate-Diagnosis and PROSTATEx datasets. The proposed model outperformed SRGAN, the state-of-the-art prostate SR model. A new task-specific similarity assessment is introduced as well. A classifier is trained for severe cancer detection and the drop in the accuracy of this model when dealing with super-resolved images is used for evaluating the ability of medical detail reconstruction of the SR models. This proposed SR model is a step towards an efficient and accurate general SR platform
Handbook of Digital Face Manipulation and Detection
This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area
Handbook of Digital Face Manipulation and Detection
This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area
Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution
Real-world face super-resolution (SR) is a highly ill-posed image restoration
task. The fully-cycled Cycle-GAN architecture is widely employed to achieve
promising performance on face SR, but prone to produce artifacts upon
challenging cases in real-world scenarios, since joint participation in the
same degradation branch will impact final performance due to huge domain gap
between real-world and synthetic LR ones obtained by generators. To better
exploit the powerful generative capability of GAN for real-world face SR, in
this paper, we establish two independent degradation branches in the forward
and backward cycle-consistent reconstruction processes, respectively, while the
two processes share the same restoration branch. Our Semi-Cycled Generative
Adversarial Networks (SCGAN) is able to alleviate the adverse effects of the
domain gap between the real-world LR face images and the synthetic LR ones, and
to achieve accurate and robust face SR performance by the shared restoration
branch regularized by both the forward and backward cycle-consistent learning
processes. Experiments on two synthetic and two real-world datasets demonstrate
that, our SCGAN outperforms the state-of-the-art methods on recovering the face
structures/details and quantitative metrics for real-world face SR. The code
will be publicly released at https://github.com/HaoHou-98/SCGAN
Octuplet Loss: Make Face Recognition Robust to Image Resolution
Image resolution, or in general, image quality, plays an essential role in
the performance of today's face recognition systems. To address this problem,
we propose a novel combination of the popular triplet loss to improve
robustness against image resolution via fine-tuning of existing face
recognition models. With octuplet loss, we leverage the relationship between
high-resolution images and their synthetically down-sampled variants jointly
with their identity labels. Fine-tuning several state-of-the-art approaches
with our method proves that we can significantly boost performance for
cross-resolution (high-to-low resolution) face verification on various datasets
without meaningfully exacerbating the performance on high-to-high resolution
images. Our method applied on the FaceTransformer network achieves 95.12% face
verification accuracy on the challenging XQLFW dataset while reaching 99.73% on
the LFW database. Moreover, the low-to-low face verification accuracy benefits
from our method. We release our code to allow seamless integration of the
octuplet loss into existing frameworks
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