High-quality Face Images Research Articles

PMSGAN: Parallel Multistage GANs for Face Image Translation.

In this article, we address the face image translation task, which aims to translate a face image of a source domain to a target domain. Although significant progress has been made by recent studies, face image translation is still a challenging task because it has more strict requirements for texture details: even a few artifacts will greatly affect the impression of generated face images. Targeting to synthesize high-quality face images with admirable visual appearance, we revisit the coarse-to-fine strategy and propose a novel p arallel m ultistage architecture on the basis of g enerative a dversarial n etworks (PMSGAN). More specifically, PMSGAN progressively learns the translation function by disintegrating the general synthesis process into multiple parallel stages that take images with gradually decreasing spatial resolution as inputs. To prompt the information exchange between various stages, a cross-stage atrous spatial pyramid (CSASP) structure is specially designed to receive and fuse the contextual information from other stages. At the end of the parallel model, we introduce a novel attention-based module that leverages multistage decoded outputs as in situ supervised attention to refine the final activations and yield the target image. Extensive experiments on several face image translation benchmarks show that PMSGAN performs considerably better than state-of-the-art approaches.

SwinDPSR: Dual-Path Face Super-Resolution Network Integrating Swin Transformer

Whether to use face priors in the face super-resolution (FSR) methods is a symmetry problem.Various face priors are used to describe the overall and local face features, making the generation of super-resolution face images expensive and laborious. FSR methods that do not require any prior information tend to focus too much on the local features of the face, ignoring the modeling of global information. To solve this problem, we propose a dual-path facial image super-resolution network (SwinDPSR) fused with Swin Transformer. The network does not require additional face priors, and it learns global face shape and local face components through two independent branches. In addition, the channel attention ECA module is used to aggregate the global and local face information in the above dual-path sub-networks, which can generate corresponding high-quality face images. The results of face super-resolution reconstruction experiments on public face datasets and a real-scene face dataset show that SwinDPSR is superior to previous advanced methods both in terms of visual effects and objective indicators. The reconstruction results are evaluated with four evaluation metrics: peak signal-to-noise ratio (PSNR), structural similarity (SSIM), learned perceptual image patch similarity (LPIPS), and mean perceptual score (MPS).

Blind Face Restoration under Extreme Conditions: Leveraging 3D-2D Prior Fusion for Superior Structural and Texture Recovery

Blind face restoration under extreme conditions involves reconstructing high-quality face images from severely degraded inputs. These input images are often in poor quality and have extreme facial poses, leading to errors in facial structure and unnatural artifacts within the restored images. In this paper, we show that utilizing 3D priors effectively compensates for structure knowledge deficiencies in 2D priors while preserving the texture details. Based on this, we introduce FREx (Face Restoration under Extreme conditions) that combines structure-accurate 3D priors and texture-rich 2D priors in pretrained generative networks for blind face restoration under extreme conditions. To fuse the different information in 3D and 2D priors, we introduce an adaptive weight module that adjusts the importance of features based on the input image's condition. With this approach, our model can restore structure-accurate and natural-looking faces even when the images have lost a lot of information due to degradation and extreme pose. Extensive experimental results on synthetic and real-world datasets validate the effectiveness of our methods.

ControlFace: Feature Disentangling for Controllable Face Swapping.

Face swapping is an intriguing and intricate task in the field of computer vision. Currently, most mainstream face swapping methods employ face recognition models to extract identity features and inject them into the generation process. Nonetheless, such methods often struggle to effectively transfer identity information, which leads to generated results failing to achieve a high identity similarity to the source face. Furthermore, if we can accurately disentangle identity information, we can achieve controllable face swapping, thereby providing more choices to users. In pursuit of this goal, we propose a new face swapping framework (ControlFace) based on the disentanglement of identity information. We disentangle the structure and texture of the source face, encoding and characterizing them in the form of feature embeddings separately. According to the semantic level of each feature representation, we inject them into the corresponding feature mapper and fuse them adequately in the latent space of StyleGAN. Owing to such disentanglement of structure and texture, we are able to controllably transfer parts of the identity features. Extensive experiments and comparisons with state-of-the-art face swapping methods demonstrate the superiority of our face swapping framework in terms of transferring identity information, producing high-quality face images, and controllable face swapping.

RestoreFormer++: Towards Real-World Blind Face Restoration From Undegraded Key-Value Pairs.

Blind face restoration aims at recovering high-quality face images from those with unknown degradations. Current algorithms mainly introduce priors to complement high-quality details and achieve impressive progress. However, most of these algorithms ignore abundant contextual information in the face and its interplay with the priors, leading to sub-optimal performance. Moreover, they pay less attention to the gap between the synthetic and real-world scenarios, limiting the robustness and generalization to real-world applications. In this work, we propose RestoreFormer++, which on the one hand introduces fully-spatial attention mechanisms to model the contextual information and the interplay with the priors, and on the other hand, explores an extending degrading model to help generate more realistic degraded face images to alleviate the synthetic-to-real-world gap. Compared with current algorithms, RestoreFormer++ has several crucial benefits. First, instead of using a multi-head self-attention mechanism like the traditional visual transformer, we introduce multi-head cross-attention over multi-scale features to fully explore spatial interactions between corrupted information and high-quality priors. In this way, it can facilitate RestoreFormer++ to restore face images with higher realness and fidelity. Second, in contrast to the recognition-oriented dictionary, we learn a reconstruction-oriented dictionary as priors, which contains more diverse high-quality facial details and better accords with the restoration target. Third, we introduce an extending degrading model that contains more realistic degraded scenarios for training data synthesizing, and thus helps to enhance the robustness and generalization of our RestoreFormer++ model. Extensive experiments show that RestoreFormer++ outperforms state-of-the-art algorithms on both synthetic and real-world datasets.

IdProv: Identity-Based Provenance for Synthetic Image Generation (Student Abstract)

Recent advancements in Generative Adversarial Networks (GANs) have made it possible to obtain high-quality face images of synthetic identities. These networks see large amounts of real faces in order to learn to generate realistic looking synthetic images. However, the concept of a synthetic identity for these images is not very well-defined. In this work, we verify identity leakage from the training set containing real images into the latent space and propose a novel method, IdProv, that uses image composition to trace the source of identity signals in the generated image.

INFORMATION TECHNOLOGY FOR PERSON IDENTIFICATION BY FACE IMAGE IN CONDITIONS OF OCCLUSION

. Face recognition is a non-invasive biometric technology, so it is of interest both to owners of small surveillance systems and for national security purposes. State-of-the-art face recognition techniques have achieved impressive results with medium- and high-quality face images, but poor performance with low-quality images. The purpose of the study is to develop and experimentally verify the information technology of face identification based on the image of a face obtained from a video stream, based on an algorithm that provides high identification results on images of low quality and resolution that contain occlusion. This paper describes research of information technology of person identification by face image, which is based on an algorithm that includes anisotropic diffusion method for image preprocessing, Gabor wavelet transform for image processing, histogram of oriented gradients (HOG) and local binary patterns in 1- dimensional space (1DLBP) for extracting an image feature vector. Since the spread of the coronavirus disease has created the problem of facial recognition in the presence of a medical mask, which is used as a preventive measure, research on facial recognition and identification technologies has become crucial for all areas of cybersecurity based on digital identity verification. Experiments with the proposed technology after applying it to occluded images from the SCface database gave a result of 85%, which increased by 2.5% after converting the image format and resolution.

TextFace: Text-to-Style Mapping Based Face Generation and Manipulation

As a sub-topic of Text-to-Image synthesis, Text-to-Face generation has a great potential in face related applications. In this paper, we propose a generic Text-to-Face framework, namely TextFace, to achieve diverse and high-quality face image generation from text description. We introduce a novel method called Text-to-Style mapping, where the text description can be directly encoded into the latent space of a pretrained StyleGAN. Guided by our text-image similarity matching and face captioning based text alignment, the textual latent code can be fed into a well-trained StyleGAN's generator, to produce diverse face images with high resolution (1024 1024). Furthermore, our model inherently supports the semantic face editing using text descriptions. Finally, experimental results quantitatively and qualitatively demonstrate the superior performance of our model.

SCTANet: A Spatial Attention-Guided CNN-Transformer Aggregation Network for Deep Face Image Super-Resolution

Numerous CNN-based algorithms have been proposed to reconstruct high-quality face images. However, the inability of convolution operation to model long-distance relationships limits the performance of the CNN-based methods. Moreover, in the high-resolution (HR) image reconstruction stage, with the well decoded feature representations, more efficient architecture design can be explored to synthesize pixel-level image details. In this work, we propose a spatial attention-guided CNN-Transformer aggregation network (SCTANet) for face image super-resolution (FSR) tasks. The core component in the deep feature extraction stage is the Hybrid Attention Aggregation (HAA) block. The HAA block has two parallel paths, one for the Residual Spatial Attention (RSA) block, the other for the Multi-scale Patch embedding and Spatial-attention Masked Transformer (MPSMT) block. The HAA block combines the strengths of CNN and transformer to effectively exploit both local and global information. For the reconstruction stage, we propose to use the Sub-pixel MLP-based Upsampling (SMU) module instead of the conventional CNN architecture. The SMU module promotes the reconstruction of pixel-level image details and reduces computational complexity. Extensive experiments on both synthetic and real-world face datasets demonstrate the superiority of our proposed SCTANet over state-of-the-art methods.

Face image-sketch synthesis via generative adversarial fusion

Face image-sketch synthesis is widely applied in law enforcement and digital entertainment fields. Despite the extensive progression in face image-sketch synthesis, there are few methods focusing on generating a color face image from a sketch. The existing methods pay less attention to learning the illumination or highlight distribution on the face region. However, the illumination is the key factor that makes the generated color face image looks vivid and realistic. Moreover, existing methods tend to employ some image preprocessing technologies and facial region patching approaches to generate high-quality face images, which results in the high complexity and memory consumption in practice. In this paper, we propose a novel end-to-end generative adversarial fusion model, called GAF, which fuses two U-Net generators and a discriminator by jointly learning the content and adversarial loss functions. In particular, we propose a parametric tanh activation function to learn and control illumination highlight distribution over faces, which is integrated between the two U-Net generators by an illumination distribution layer. Additionally, we fuse the attention mechanism into the second U-Net generator of GAF to keep the identity consistency and refine the generated facial details. The qualitative and quantitative experiments on the public benchmark datasets show that the proposed GAF has better performance than existing image-sketch synthesis methods in synthesized face image quality (FSIM) and face recognition accuracy (NLDA). Meanwhile, the good generalization ability of GAF has also been verified. To further demonstrate the reliability and authenticity of face images generated using GAF, we use the generated face image to attack the well-known face recognition system. The result shows that the face images generated by GAF can maintain identity consistency and well maintain everyone’s unique facial characteristics, which can be further used in the benchmark of facial spoofing. Moreover, the experiments are implemented to verify the effectiveness and rationality of the proposed parametric tanh activation function and attention mechanism in GAF.

Face Image Inpainting Using Attribute Guided

Face image inpainting is becoming more and more significant in the practical fields of criminal investigation, cosmetic plastic surgery, and security protection. But most of traditional existing work is based on diffusion and texture methods, failing to effectively use the embedding attribute knowledge and semantic information of face images. It is difficult to ensure the authenticity and preserve semantic consistency at the same time. To tackle this problem, a face image inpainting method based on attribute control is proposed. First, a modified generative adversarial network is proposed to restore face images and control facial features by adding additional face attribute information and a classification network. Then an attention module based on multi-scale feature fusion is proposed, through increasing the receptive field of the neural network to obtain more global features to improve face image inpainting quality without increasing the arithmetic complexity. Finally, combining the four evaluation metrics, MAE, PSNR, SSIM, and FID, the experiments on the CelebA dataset show that the proposed method can not only generate high-quality face image inpainting results steered by semantic information, but also generate corresponding facial features based on attribute control.

Joint Face Image Restoration and Frontalization for Recognition

In real-world scenarios, many factors may harm face recognition performance, e.g., large pose, bad illumination,low resolution, blur and noise. To address these challenges, previous efforts usually first restore the low-quality faces to high-quality ones and then perform face recognition. However, most of these methods are stage-wise, which is sub-optimal and deviates from the reality. In this paper, we address all these challenges jointly for unconstrained face recognition. We propose an Multi-Degradation Face Restoration (MDFR) model to restore frontalized high-quality faces from the given low-quality ones under arbitrary facial poses, with three distinct novelties. First, MDFR is a well-designed encoder-decoder architecture which extracts feature representation from an input face image with arbitrary low-quality factors and restores it to a high-quality counterpart. Second, MDFR introduces a pose residual learning strategy along with a 3D-based Pose Normalization Module (PNM), which can perceive the pose gap between the input initial pose and its real-frontal pose to guide the face frontalization. Finally, MDFR can generate frontalized high-quality face images by a single unified network, showing a strong capability of preserving face identity. Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks demonstrate the superiority of MDFR over state-of-the-art methods on both face frontalization and face restoration.

Deep Rival Penalized Competitive Learning for low-resolution face recognition

Current face recognition tasks are usually carried out on high-quality face images, but in reality, most face images are captured under unconstrained or poor conditions, e.g., by video surveillance. Existing methods are featured by learning data uncertainty to avoid overfitting the noise, or by adding margins to the angle or cosine space of the normalized softmax loss to penalize the target logit, which enforces intra-class compactness and inter-class discrepancy. In this paper, we propose a deep Rival Penalized Competitive Learning (RPCL) for deep face recognition in low-resolution (LR) images. Inspired by the idea of the RPCL, our method further enforces regulation on the rival logit, which is defined as the largest non-target logit for an input image. Different from existing methods that only consider penalization on the target logit, our method not only strengthens the learning towards the target label, but also enforces a reverse direction, i.e., becoming de-learning, away from the rival label. Comprehensive experiments demonstrate that our method improves the existing state-of-the-art methods to be very robust for LR face recognition.

ShapeEditor: A StyleGAN Encoder for Stable and High Fidelity Face Swapping.

With the continuous development of deep-learning technology, ever more advanced face-swapping methods are being proposed. Recently, face-swapping methods based on generative adversarial networks (GANs) have realized many-to-many face exchanges with few samples, which advances the development of this field. However, the images generated by previous GAN-based methods often show instability. The fundamental reason is that the GAN in these frameworks is difficult to converge to the distribution of face space in training completely. To solve this problem, we propose a novel face-swapping method based on pretrained StyleGAN generator with a stronger ability of high-quality face image generation. The critical issue is how to control StyleGAN to generate swapped images accurately. We design the control strategy of the generator based on the idea of encoding and decoding and propose an encoder called ShapeEditor to complete this task. ShapeEditor is a two-step encoder used to generate a set of coding vectors that integrate the identity and attribute of the input faces. In the first step, we extract the identity vector of the source image and the attribute vector of the target image; in the second step, we map the concatenation of the identity vector and attribute vector onto the potential internal space of StyleGAN. Extensive experiments on the test dataset show that the results of the proposed method are not only superior in clarity and authenticity than other state-of-the-art methods but also sufficiently integrate identity and attribute.

Illumination Analysis of Deep Face Recognition

Illumination variation significantly affects the performance of face recognition algorithms. However, it is difficult to construct a database that meets the demand of illumination analysis with current face image database construction technology. The influence of illumination variation on deep face recognition based on 3D face models is investigated. First a new method is presented to produce face images under arbitrary illuminations by using a 3D face model based on face basis image representation theory proposed. Then the database used for illumination analysis is constructed. Afterwards, an optimal way is explored to select illuminations used for constructing a high quality face image database by analyzing the impacts of different illumination sampling strategies on the performances of face recognition models. With the advantage of accurate illumination labeling of virtual data, the performances of different illumination labeling methods on the training of face recognition network are tested on a multi-task learning framework. Through experiments on the effects of different lighting sampling schemes and labeling methods on the performances of face recognition models carried out on virtual data and real data, it is concluded that using an appropriate amount of lightings of basis images to build a database is an effective lighting sampling scheme, and accurate lighting annotation can help further improve the face recognition rate, the corresponding recognition models have face recognition accuracy rates of more than 98% on virtual and real monitoring datasets which contain face images captured under very challenging lighting conditions. Proposed work improve the performance of deep face recognition models, and facilitate the construction of high quality face image databases with various illuminations.

Real‐world super‐resolution of face‐images from surveillance cameras

Most existing face image Super-Resolution (SR) methods assume that the Low-Resolution (LR) images were artificially downsampled from High-Resolution (HR) images with bicubic interpolation. This operation changes the natural image characteristics and reduces noise. Hence, SR methods trained on such data most often fail to produce good results when applied to real LR images. To solve this problem, a novel framework for the generation of realistic LR/HR training pairs is proposed. The framework estimates realistic blur kernels, noise distributions, and JPEG compression artifacts to generate LR images with similar image characteristics as the ones in the source domain. This allows to train an SR model using high-quality face images as Ground-Truth (GT). For better perceptual quality, a Generative Adversarial Network (GAN) based SR model is used, where the commonly used VGG-loss [1] is exchanged with LPIPS-loss [2]. Experimental results on both real and artificially corrupted face images show that our method results in more detailed reconstructions with less noise compared to the existing State-of-the-Art (SoTA) methods. In addition, it is shown that the traditional non-reference Image Quality Assessment (IQA) methods fail to capture this improvement and demonstrate that the more recent NIMA metric [3] correlates better with human perception via Mean Opinion Rank (MOR).

Cali-sketch: Stroke calibration and completion for high-quality face image generation from human-like sketches

Image generation has received increasing attention because of its wide application in security and entertainment. Sketch-based face generation brings more fun and better quality of image generation due to supervised interaction. However, when a sketch poorly aligned with the true face is given as input, existing supervised image-to-image translation methods often cannot generate acceptable photo-realistic face images. To address this problem, in this paper we propose Cali-Sketch, a human-like-sketch to photo-realistic-image generation method. Cali-Sketch explicitly models stroke calibration and image generation using two constituent networks: a Stroke Calibration Network (SCN), which calibrates strokes of facial features and enriches facial details while preserving the original intent features; and an Image Synthesis Network (ISN), which translates the calibrated and enriched sketches to photo-realistic face images. In this way, we manage to decouple a difficult cross-domain translation problem into two easier steps. Extensive experiments verify that the face photos generated by Cali-Sketch are both photo-realistic and faithful to the input sketches, compared with state-of-the-art methods.

System for Inception of Images From An Under Drawing

Catching the criminal on the basis of eyewitness description sketches which are generated by any software or hand drawn this technique becomes useful when there is deficiency of evidence. Recognising the sketches with face photos to find the results. A facial recognition is a technology capable of verifying a person by using deep learning is the part of data science and this creating more importance in law enforcement agencies. Deep learning generally used for recognising application like voice recognition, face recognition, audio and video recognition. In this system, we are working larger dataset because deep learning not useful for small amount of data. We first learn feature embeddings of key face components, and push corresponding parts of input sketches towards underlying component manifolds defined by the feature vectors of face component samples. We also propose another deep neural network to learn the mapping from the embedded component features to realistic images with multi-channel feature maps as intermediate results to improve the information flow. Our method essentially uses input sketches as soft constraints and is thus able to produce high-quality face images even from rough and/or incomplete sketches. Our tool is easy to use even for non-artists, while still supporting fine-grained control of shape details. Both qualitative and quantitative evaluations show the superior generation ability of our system to existing and alternative solutions. The usability and expressiveness of our system are confirmed by a user study.

High-quality face image generation using particle swarm optimization-based generative adversarial networks

Face image generation based on generative adversarial networks (GAN) is a hot research topic in computer vision. Existing GAN-based algorithms are constrained by training instability and mode collapse. Considering that particle swarm optimization (PSO) algorithm has good global optimization ability, we propose a generation antagonism network based on PSO algorithm to improve the training stability. More specifically, the inertia weight of particle swarm is improved by using the parameters of particle representative generator network in particle swarm optimization, and the aggregation degree of particles is judged to ensure the optimization ability of particle swarm optimization and the diversity of population. In addition, we evaluate the performance of the generator by generating quality and diversity evaluation functions to better guide the iterative updating of particle swarm optimization. Our face image generation experiment is conducted on CelebA dataset and experimental result shows the effectiveness and robustness of our proposed method.

Human face super-resolution on poor quality surveillance video footage

Most super-resolution (SR) methods proposed to date do not use real ground-truth high-resolution (HR) and low-resolution (LR) image pairs; instead, the vast majority of methods use synthetic LR images generated from the HR images. This approach yields excellent performance on synthetic datasets, but on real-world poor quality surveillance video footage, they suffer from performance degradation. A promising alternative is to apply recent advances in style transfer for unpaired datasets, but state-of-the-art work along these lines has used LR images and HR images from completely different datasets, introducing more variation between the HR and LR domains than necessary. In this paper, we propose methods that overcome both of these shortcomings, applying unpaired style transfer learning methods to face SR but using HR and LR datasets that share important properties. The key is to acquire roughly paired training data from a high-quality main stream and a lower-quality sub-stream of the same IP camera. Based on this principle, we have constructed four datasets comprising more than 400 people, with 1–15 weakly aligned real HR–LR pairs for each subject. We adopt a cycle generative adversarial networks (Cycle GANs) approach that produces impressive super-resolved images for low-quality test images never seen during training. Experiments prove the efficacy of the method. The approach to face SR advocated for in this paper makes possible many real-world applications requiring the extraction of high-quality face images from low-resolution video streams such as those produced by security cameras. Developers of diverse applications such as face recognition, 3D face reconstruction, face alignment, face parsing, human–computer interaction, remote sensing, and access control will benefit from the methods introduced in this work.