A CORRELATIVE TWO-STEP APPROACH TO HALLUCINATING FACES

2014 ◽  
Vol 28 (08) ◽  
pp. 1454004 ◽  
Author(s):  
HUANXI LIU ◽  
TIANHONG ZHU
Keyword(s):  
High Resolution ◽  
Computational Efficiency ◽  
Computational Cost ◽  
Image Inpainting ◽  
Reconstruction Algorithm ◽  
Face Image ◽  
Face Hallucination ◽  
Face Images ◽  
Steerable Pyramid Decomposition ◽  
Global And Local

Face hallucination is to synthesize high-resolution face image from the input low-resolution one. Although many two-step learning-based face hallucination approaches have been developed, they suffer from the expensive computational cost due to the separate calculation of the global and local models. To overcome this problem, we propose a correlative two-step learning-based face hallucination approach which bridges the gap between the global model and the local model. In the global phase, we build a global face hallucination framework by combining the steerable pyramid decomposition and the reconstruction. In the residue compensation phase, based on the combination weights and constituent samples obtained in the global phase, a residue face image is synthesized by the neighbor reconstruction algorithm to compensate the hallucinated global face image with subtle facial features. The ultimate hallucinated result is synthesized by adding the residue face image to the global face image. Compared with existing methods, in the global phase, our global face image is more similar to the original high-resolution face image. Furthermore, in the residue compensation phase, we use the combination weights and constituent samples obtained in the global phase to compute the residue face image, by which the computational efficiency can be greatly improved without compromising the quality of facial details. The experimental results and comparisons demonstrate that our approach can not only generate convincible high-resolution face images efficiently, but also has high computational efficiency. Furthermore, our proposed approach can be used to restore the damaged face images in image inpainting. The efficacy of our approach is validated by recovering the damaged face images with visually good results.

Adapted discriminative coupled mappings for low-resolution face recognition with one-shot

2021 ◽  
pp. 1-15
Author(s):  
Yongjie Chu ◽  
Touqeer Ahmad ◽  
Lindu Zhao
Keyword(s):  
Face Recognition ◽  
Law Enforcement ◽  
High Resolution ◽  
Domain Adaptation ◽  
Face Image ◽  
Discriminative Learning ◽  
Low Resolution ◽  
Face Images ◽  
World Environment

Low-resolution face recognition with one-shot is a prevalent problem encountered in law enforcement, where it generally requires to recognize the low-resolution face images captured by surveillance cameras with the only one high-resolution profile face image in the database. The problem is very tough because the available samples is quite few and the quality of unknown images is quite low. To effectively address this issue, this paper proposes Adapted Discriminative Coupled Mappings (AdaDCM) approach, which integrates domain adaptation and discriminative learning. To achieve good domain adaptation performance for small size dataset, a new domain adaptation technique called Bidirectional Locality Matching-based Domain Adaptation (BLM-DA) is first developed. Then the proposed AdaDCM is formulated by unifying BLM-DA and discriminative coupled mappings into a single framework. AdaDCM is extensively evaluated on FERET, LFW, and SCface databases, which includes LR face images obtained in constrained, unconstrained, and real-world environment. The promising results on these datasets demonstrate the effectiveness of AdaDCM in LR face recognition with one-shot.

scholarly journals 3D Face Image Inpainting with Generative Adversarial Nets

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Tongxin Wei ◽  
Qingbao Li ◽  
Jinjin Liu ◽  
Ping Zhang ◽  
Zhifeng Chen
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Image Inpainting ◽  
Face Image ◽  
Edge Information ◽  
3D Face ◽  
Face Images ◽  
The Face ◽  
Match Effect ◽  
Traditional Image

In the process of face recognition, face acquisition data is seriously distorted. Many face images collected are blurred or even missing. Faced with so many problems, the traditional image inpainting was based on structure, while the current popular image inpainting method is based on deep convolutional neural network and generative adversarial nets. In this paper, we propose a 3D face image inpainting method based on generative adversarial nets. We identify two parallels of the vector to locate the planer positions. Compared with the previous, the edge information of the missing image is detected, and the edge fuzzy inpainting can achieve better visual match effect. We make the face recognition performance dramatically boost.

scholarly journals An Adaptive Face Image Inpainting Algorithm Based on Feature Symmetry

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 190
Author(s):  
Zuodong Niu ◽  
Handong Li ◽  
Yao Li ◽  
Yingjie Mei ◽  
Jing Yang
Keyword(s):  
Image Restoration ◽  
Color Space ◽  
Image Inpainting ◽  
Face Image ◽  
Search Range ◽  
Visual Evaluation ◽  
Point Distribution ◽  
Face Images ◽  
Face Features ◽  
The Face

Face image inpainting technology is an important research direction in image restoration. When the current image restoration methods repair the damaged areas of face images with weak texture, there are problems such as low accuracy of face image decomposition, unreasonable restoration structure, and degradation of image quality after inpainting. Therefore, this paper proposes an adaptive face image inpainting algorithm based on feature symmetry. Firstly, we locate the feature points of the face, and segment the face into four feature parts based on the feature point distribution to define the feature search range. Then, we construct a new mathematical model, introduce feature symmetry to improve priority calculation, and increase the reliability of priority calculation. After that, in the process of searching for matching blocks, we accurately locate similar feature blocks according to the relative position and symmetry criteria of the target block and various feature parts of the face. Finally, we introduced the HSV (Hue, Saturation, Value) color space to determine the best matching block according to the chroma and brightness of the sample, reduce the repair error, and complete the face image inpainting. During the experiment, we firstly performed visual evaluation and texture analysis on the inpainting face image, and the results show that the face image inpainting by our algorithm maintained the consistency of the face structure, and the visual observation was closer to the real face features. Then, we used the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as objective evaluation indicators; among the five sample face images inpainting results given in this paper, our method was better than the reference methods, and the average PSNR value improved from 2.881–5.776 dB using our method when inpainting 100 face images. Additionally, we used the time required for inpainting the unit pixel to evaluate the inpainting efficiency, and it was improved by 12%–49% with our method when inpainting 100 face images. Finally, by comparing the face image inpainting experiments with the generative adversary network (GAN) algorithm, we discuss some of the problems with the method in this paper based on graphics in repairing face images with large areas of missing features.

scholarly journals A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

2020 ◽  
Vol 10 (2) ◽  
pp. 718 ◽  
Author(s):  
K. Lakshminarayanan ◽  
R. Santhana Krishnan ◽  
E. Golden Julie ◽  
Y. Harold Robinson ◽  
Raghvendra Kumar ◽  
...  
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Expectation Maximization ◽  
Super Resolution ◽  
Integrated Approach ◽  
Face Hallucination ◽  
Resolution Image ◽  
High Resolution Image ◽  
Face Images ◽  
Resolution Algorithm

This paper proposed and verified a new integrated approach based on the iterative super-resolution algorithm and expectation-maximization for face hallucination, which is a process of converting a low-resolution face image to a high-resolution image. The current sparse representation for super resolving generic image patches is not suitable for global face images due to its lower accuracy and time-consumption. To solve this, in the new method, training global face sparse representation was used to reconstruct images with misalignment variations after the local geometric co-occurrence matrix. In the testing phase, we proposed a hybrid method, which is a combination of the sparse global representation and the local linear regression using the Expectation Maximization (EM) algorithm. Therefore, this work recovered the high-resolution image of a corresponding low-resolution image. Experimental validation suggested improvement of the overall accuracy of the proposed method with fast identification of high-resolution face images without misalignment.

scholarly journals Learning to Hallucinate Face Images via Component Generation and Enhancement

2017 ◽  
Author(s):  
Yibing Song ◽  
Jiawei Zhang ◽  
Shengfeng He ◽  
Linchao Bao ◽  
Qingxiong Yang
Keyword(s):  
High Resolution ◽  
State Of The Art ◽  
Ground Truth ◽  
Input Image ◽  
Two Stage ◽  
Face Hallucination ◽  
Fine Grained ◽  
Second Stage ◽  
Training Images ◽  
Face Images

We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Second, we synthesize fine-grained facial structures from high resolution training images. The details of these structures are transferred into facial components for enhancement. Therefore, we generate facial components to approximate ground truth global appearance in the first stage and enhance them through recovering details in the second stage. The experiments demonstrate that our method performs favorably against state-of-the-art methods.

scholarly journals Prefracture and cold-fracture images of yeast plasma membranes.

1980 ◽  
Vol 86 (1) ◽  
pp. 113-122 ◽  
Author(s):  
R L Steere ◽  
E F Erbe ◽  
J M Moseley
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Plasma Membrane ◽  
High Resolution ◽  
Plasma Membranes ◽  
Physiological Condition ◽  
Face Image ◽  
Yeast Cells ◽  
Yeast Saccharomyces Cerevisiae ◽  
Face Images ◽  
Single Plasma

Fracture-temperature related differences in the ultrastructure of plasmalemma P faces of freeze-fractured baker's yeast (Saccharomyces cerevisiae) have been observed in high-resolution replicas prepared in freeze-etch systems pumped to 2 X 10(-7) torr in which the specimens were protected from contamination by use of liquid nitrogen-cooled shrouds. Two major P-face images were observed regardless of the source of the yeast, the age of the culture, the growth temperature, the physiological condition, or the suspending medium used: (a) a "cold-fracture image" with many strands closely associuated with tubelike particles (essentially the same image as those previously published for yeast freeze-fractured at 77 degrees K), and (b) a "prefracture image" characterized by the presence of more distinct tubelike particles with few or no associated strands (for aging cultures, the image recently referred to as "paracrystalline arrays" of "craterlike particles"). Both types of P-face image can be found in separate areas of single replicas and occasionally even within a single plasma membrane. Whereas portions of replicas known to be fractured at any temperature colder than 218 degrees K reveal only the cold-fracture image, prefracture images are found in cells intentionally fractured at 243 degrees K and in cracks or fissures which develop during the freezing of other specimens. These findings demonstrate that the prefracture image results from the fracturing of specimens at some temperature above 230 degrees K, no t from fracturing specimens at some temperature between 173 degrees and 77 degrees K, and not from the use of "starved" yeast cells.

scholarly journals Renormalization group theory of molecular dynamics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daiji Ichishima ◽  
Yuya Matsumura
Keyword(s):  
Molecular Dynamics ◽  
Renormalization Group ◽  
Critical Exponent ◽  
Computational Efficiency ◽  
Large Scale ◽  
Dissipative Particle Dynamics ◽  
Computational Cost ◽  
Coarse Graining ◽  
Spatial Dimension ◽  
Deborah Number

AbstractLarge scale computation by molecular dynamics (MD) method is often challenging or even impractical due to its computational cost, in spite of its wide applications in a variety of fields. Although the recent advancement in parallel computing and introduction of coarse-graining methods have enabled large scale calculations, macroscopic analyses are still not realizable. Here, we present renormalized molecular dynamics (RMD), a renormalization group of MD in thermal equilibrium derived by using the Migdal–Kadanoff approximation. The RMD method improves the computational efficiency drastically while retaining the advantage of MD. The computational efficiency is improved by a factor of $$2^{n(D+1)}$$ 2 n ( D + 1 ) over conventional MD where D is the spatial dimension and n is the number of applied renormalization transforms. We verify RMD by conducting two simulations; melting of an aluminum slab and collision of aluminum spheres. Both problems show that the expectation values of physical quantities are in good agreement after the renormalization, whereas the consumption time is reduced as expected. To observe behavior of RMD near the critical point, the critical exponent of the Lennard-Jones potential is extracted by calculating specific heat on the mesoscale. The critical exponent is obtained as $$\nu =0.63\pm 0.01$$ ν = 0.63 ± 0.01 . In addition, the renormalization group of dissipative particle dynamics (DPD) is derived. Renormalized DPD is equivalent to RMD in isothermal systems under the condition such that Deborah number $$De\ll 1$$ D e ≪ 1 .

scholarly journals Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Surveillance Video ◽  
Low Resolution ◽  
Model Free ◽  
Face Images

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

scholarly journals Enhanced use of gaze cue in a face-following task after brief trial experience in individuals with autism spectrum disorder

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Takao Fukui ◽  
Mrinmoy Chakrabarty ◽  
Misako Sano ◽  
Ari Tanaka ◽  
Mayuko Suzuki ◽  
...  
Keyword(s):  
Autism Spectrum ◽  
Face Image ◽  
Fixation Time ◽  
Gaze Shift ◽  
The Gaze ◽  
Face Images ◽  
Trial Experience ◽  
Short Period ◽  
The Face ◽  
Gaze Cues

AbstractEye movements toward sequentially presented face images with or without gaze cues were recorded to investigate whether those with ASD, in comparison to their typically developing (TD) peers, could prospectively perform the task according to gaze cues. Line-drawn face images were sequentially presented for one second each on a laptop PC display, and the face images shifted from side-to-side and up-and-down. In the gaze cue condition, the gaze of the face image was directed to the position where the next face would be presented. Although the participants with ASD looked less at the eye area of the face image than their TD peers, they could perform comparable smooth gaze shift to the gaze cue of the face image in the gaze cue condition. This appropriate gaze shift in the ASD group was more evident in the second half of trials in than in the first half, as revealed by the mean proportion of fixation time in the eye area to valid gaze data in the early phase (during face image presentation) and the time to first fixation on the eye area. These results suggest that individuals with ASD may benefit from the short-period trial experiment by enhancing the usage of gaze cue.

An Efficient Dual-Resolution Approach for Ensemble Data Assimilation and Tests with Simulated Doppler Radar Data

2008 ◽  
Vol 136 (3) ◽  
pp. 945-963 ◽  
Author(s):  
Jidong Gao ◽  
Ming Xue
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Radial Velocity ◽  
Computational Cost ◽  
Radar Data ◽  
Horizontal Resolution ◽  
Velocity Data ◽  
Background Error ◽  
Cross Covariance ◽  
Radial Velocity Data

Abstract A new efficient dual-resolution (DR) data assimilation algorithm is developed based on the ensemble Kalman filter (EnKF) method and tested using simulated radar radial velocity data for a supercell storm. Radar observations are assimilated on both high-resolution and lower-resolution grids using the EnKF algorithm with flow-dependent background error covariances estimated from the lower-resolution ensemble. It is shown that the flow-dependent and dynamically evolved background error covariances thus estimated are effective in producing quality analyses on the high-resolution grid. The DR method has the advantage of being able to significantly reduce the computational cost of the EnKF analysis. In the system, the lower-resolution ensemble provides the flow-dependent background error covariance, while the single-high-resolution forecast and analysis provides the benefit of higher resolution, which is important for resolving the internal structures of thunderstorms. The relative smoothness of the covariance obtained from the lower 4-km-resolution ensemble does not appear to significantly degrade the quality of analysis. This is because the cross covariance among different variables is of first-order importance for “retrieving” unobserved variables from the radar radial velocity data. For the DR analysis, an ensemble size of 40 appears to be a reasonable choice with the use of a 4-km horizontal resolution in the ensemble and a 1-km resolution in the high-resolution analysis. Several sensitivity tests show that the DR EnKF system is quite robust to different observation errors. A 4-km thinned data resolution is a compromise that is acceptable under the constraint of real-time applications. A data density of 8 km leads to a significant degradation in the analysis.

Sign in / Sign up

Export Citation Format

Share Document