21 research outputs found
Boosting Cross-Quality Face Verification using Blind Face Restoration
In recent years, various Blind Face Restoration (BFR) techniques were
developed. These techniques transform low quality faces suffering from multiple
degradations to more realistic and natural face images with high perceptual
quality. However, it is crucial for the task of face verification to not only
enhance the perceptual quality of the low quality images but also to improve
the biometric-utility face quality metrics. Furthermore, preserving the
valuable identity information is of great importance. In this paper, we
investigate the impact of applying three state-of-the-art blind face
restoration techniques namely, GFP-GAN, GPEN and SGPN on the performance of
face verification system under very challenging environment characterized by
very low quality images. Extensive experimental results on the recently
proposed cross-quality LFW database using three state-of-the-art deep face
recognition models demonstrate the effectiveness of GFP-GAN in boosting
significantly the face verification accuracy.Comment: paper accepted at BIOSIG 2023 conferenc
Multiple Exemplars-based Hallucinationfor Face Super-resolution and Editing
Given a really low-resolution input image of a face (say 16x16 or 8x8
pixels), the goal of this paper is to reconstruct a high-resolution version
thereof. This, by itself, is an ill-posed problem, as the high-frequency
information is missing in the low-resolution input and needs to be
hallucinated, based on prior knowledge about the image content. Rather than
relying on a generic face prior, in this paper, we explore the use of a set of
exemplars, i.e. other high-resolution images of the same person. These guide
the neural network as we condition the output on them. Multiple exemplars work
better than a single one. To combine the information from multiple exemplars
effectively, we introduce a pixel-wise weight generation module. Besides
standard face super-resolution, our method allows to perform subtle face
editing simply by replacing the exemplars with another set with different
facial features. A user study is conducted and shows the super-resolved images
can hardly be distinguished from real images on the CelebA dataset. A
qualitative comparison indicates our model outperforms methods proposed in the
literature on the CelebA and WebFace dataset.Comment: accepted in ACCV 202
DiffBFR: Bootstrapping Diffusion Model Towards Blind Face Restoration
Blind face restoration (BFR) is important while challenging. Prior works
prefer to exploit GAN-based frameworks to tackle this task due to the balance
of quality and efficiency. However, these methods suffer from poor stability
and adaptability to long-tail distribution, failing to simultaneously retain
source identity and restore detail. We propose DiffBFR to introduce Diffusion
Probabilistic Model (DPM) for BFR to tackle the above problem, given its
superiority over GAN in aspects of avoiding training collapse and generating
long-tail distribution. DiffBFR utilizes a two-step design, that first restores
identity information from low-quality images and then enhances texture details
according to the distribution of real faces. This design is implemented with
two key components: 1) Identity Restoration Module (IRM) for preserving the
face details in results. Instead of denoising from pure Gaussian random
distribution with LQ images as the condition during the reverse process, we
propose a novel truncated sampling method which starts from LQ images with part
noise added. We theoretically prove that this change shrinks the evidence lower
bound of DPM and then restores more original details. With theoretical proof,
two cascade conditional DPMs with different input sizes are introduced to
strengthen this sampling effect and reduce training difficulty in the
high-resolution image generated directly. 2) Texture Enhancement Module (TEM)
for polishing the texture of the image. Here an unconditional DPM, a LQ-free
model, is introduced to further force the restorations to appear realistic. We
theoretically proved that this unconditional DPM trained on pure HQ images
contributes to justifying the correct distribution of inference images output
from IRM in pixel-level space. Truncated sampling with fractional time step is
utilized to polish pixel-level textures while preserving identity information
Learning Flow-based Feature Warping for Face Frontalization with Illumination Inconsistent Supervision
Despite recent advances in deep learning-based face frontalization methods,
photo-realistic and illumination preserving frontal face synthesis is still
challenging due to large pose and illumination discrepancy during training. We
propose a novel Flow-based Feature Warping Model (FFWM) which can learn to
synthesize photo-realistic and illumination preserving frontal images with
illumination inconsistent supervision. Specifically, an Illumination Preserving
Module (IPM) is proposed to learn illumination preserving image synthesis from
illumination inconsistent image pairs. IPM includes two pathways which
collaborate to ensure the synthesized frontal images are illumination
preserving and with fine details. Moreover, a Warp Attention Module (WAM) is
introduced to reduce the pose discrepancy in the feature level, and hence to
synthesize frontal images more effectively and preserve more details of profile
images. The attention mechanism in WAM helps reduce the artifacts caused by the
displacements between the profile and the frontal images. Quantitative and
qualitative experimental results show that our FFWM can synthesize
photo-realistic and illumination preserving frontal images and performs
favorably against the state-of-the-art results.Comment: ECCV 2020. Code is available at: https://github.com/csyxwei/FFW