scholarly journals Real-World Automatic Makeup via Identity Preservation Makeup Net

2020 ◽  
Author(s):  
Zhikun Huang ◽  
Zhedong Zheng ◽  
Chenggang Yan ◽  
Hongtao Xie ◽  
Yaoqi Sun ◽  
...  
Keyword(s):  
Real World ◽  
Face Image ◽  
Task Demands ◽  
Reference Image ◽  
Target Image ◽  
Identity Preservation ◽  
The Real ◽  
Face Images ◽  
The Face ◽  
Robust System

This paper focuses on the real-world automatic makeup problem. Given one non-makeup target image and one reference image, the automatic makeup is to generate one face image, which maintains the original identity with the makeup style in the reference image. In the real-world scenario, face makeup task demands a robust system against the environmental variants. The two main challenges in real-world face makeup could be summarized as follow: first, the background in real-world images is complicated. The previous methods are prone to change the style of background as well; second, the foreground faces are also easy to be affected. For instance, the ``heavy'' makeup may lose the discriminative information of the original identity. To address these two challenges, we introduce a new makeup model, called Identity Preservation Makeup Net (IPM-Net), which preserves not only the background but the critical patterns of the original identity. Specifically, we disentangle the face images to two different information codes, i.e., identity content code and makeup style code. When inference, we only need to change the makeup style code to generate various makeup images of the target person. In the experiment, we show the proposed method achieves not only better accuracy in both realism (FID) and diversity (LPIPS) in the test set, but also works well on the real-world images collected from the Internet.

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.

scholarly journals Real-world size of objects serves as an axis of object space

2021 ◽  
Author(s):  
Taicheng Huang ◽  
Yiying Song ◽  
Jia Liu
Keyword(s):  
Real World ◽  
Temporal Cortex ◽  
Principal Component ◽  
Physical World ◽  
Task Demands ◽  
Deep Convolutional Neural Networks ◽  
Object Space ◽  
The Real ◽  
Ventral Temporal Cortex ◽  
Dimensional Object

Our mind can represent various objects from the physical world metaphorically into an abstract and complex high-dimensional object space, with a finite number of orthogonal axes encoding critical object features. Previous fMRI studies have shown that the middle fusiform sulcus in the ventral temporal cortex separates the real-world small-size map from the large-size map. Here we asked whether the feature of objects' real-world size constructed an axis of object space with deep convolutional neural networks (DCNNs) based on three criteria of sensitivity, independence and necessity that are impractical to be examined altogether with traditional approaches. A principal component analysis on features extracted by the DCNNs showed that objects' real-world size was encoded by an independent component, and the removal of this component significantly impaired DCNN's performance in recognizing objects. By manipulating stimuli, we found that the shape and texture of objects, rather than retina size, co-occurrence and task demands, accounted for the representation of the real-world size in the DCNNs. A follow-up fMRI experiment on humans further demonstrated that the shape, but not the texture, was used to infer the real-world size of objects in humans. In short, with both computational modeling and empirical human experiments, our study provided the first evidence supporting the feature of objects' real-world size as an axis of object space, and devised a novel paradigm for future exploring the structure of object space.

Prioritizing Attention over Privacy: Analysis of Face Image Posting Behavior in Online Direct-to-Consumer Genetic Testing Forum (Preprint)

2021 ◽  
Author(s):  
Yongtai Liu ◽  
Zhijun Yin ◽  
Zhiyu Wan ◽  
Chao Yan ◽  
Weiyi Xia ◽  
...  
Keyword(s):  
Genetic Testing ◽  
Language Processing ◽  
Face Image ◽  
Online Forums ◽  
The Public ◽  
Image Sharing ◽  
Direct To Consumer ◽  
Face Images ◽  
Identity Disclosure ◽  
The Face

BACKGROUND As direct-to-consumer genetic testing (DTC-GT) services have grown in popularity, the public has increasingly relied upon online forums to discuss and share their test results. Initially, users did so under a pseudonym, but more recently, they have included face images when discussing DTC-GT results. When these images truthfully represent a user, they reveal the identity of the corresponding individual. Various studies have shown that sharing images in social media tends to elicit more replies. However, users who do this clearly forgo their privacy. OBJECTIVE This study aimed to investigate the face image sharing behavior of DTC-GT users in an online environment and determine if there exists the association between face image sharing and the attention received from others. METHODS This study focused on r/23andme, a subreddit dedicated to discussing DTC-GT results and their implications. We applied natural language processing to infer the themes associated with posts that included a face image. We applied a regression analysis to learn the association between the attention that a post received, in terms of the number of comments and karma scores (defined as the number of upvotes minus the number of downvotes), and whether the post contains a face image. RESULTS We collected over 15,000 posts from the r/23andme subreddit published between 2012 and 2020. Face image posting began in late 2019 and grew rapidly, with over 800 individuals’ revealing their faces by early 2020. The topics in posts including a face were primarily about sharing or discussing ancestry composition, and sharing family reunion photos with relatives discovered via DTC-GT. On average, posts including a face received 60% (5/8) more comments than other posts, and these posts had karma scores 2.4 times higher than other posts. CONCLUSIONS DTC-GT consumers in the r/23andme subreddit are increasingly posting face images and testing reports on social platforms. The association between face image posting and a greater level of attention suggests that people are forgoing their privacy in exchange for attention from others. To mitigate the risk of face image posting, platforms, or at least subreddit organizers, should inform users about the consequence of such behavior for identity disclosure.

scholarly journals Exploring sensorimotor performance and user experience within a virtual reality golf putting simulator

2020 ◽  
Author(s):  
David J. Harris ◽  
Gavin Buckingham ◽  
Mark R. Wilson ◽  
Jack Brookes ◽  
Faisal Mushtaq ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Construct Validity ◽  
Real World ◽  
High Performance ◽  
Task Demands ◽  
Self Report ◽  
The Real ◽  
Task Load ◽  
Golf Putting ◽  
Effective Transfer

Abstract In light of recent advances in technology, there has been growing interest in virtual reality (VR) simulations for training purposes in a range of high-performance environments, from sport to nuclear decommissioning. For a VR simulation to elicit effective transfer of training to the real-world, it must provide a sufficient level of validity, that is, it must be representative of the real-world skill. In order to develop the most effective simulations, assessments of validity should be carried out prior to implementing simulations in training. The aim of this work was to test elements of the physical fidelity, psychological fidelity and construct validity of a VR golf putting simulation. Self-report measures of task load and presence in the simulation were taken following real and simulated golf putting to assess psychological and physical fidelity. The performance of novice and expert golfers in the simulation was also compared as an initial test of construct validity. Participants reported a high degree of presence in the simulation, and there was little difference between real and virtual putting in terms of task demands. Experts performed significantly better in the simulation than novices (p = .001, d = 1.23), and there was a significant relationship between performance on the real and virtual tasks (r = .46, p = .004). The results indicated that the simulation exhibited an acceptable degree of construct validity and psychological fidelity. However, some differences between the real and virtual tasks emerged, suggesting further validation work is required.

Face and Eye Detection

2011 ◽  
pp. 5-44 ◽  
Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  
Face Detection ◽  
Facial Expression Recognition ◽  
Face Image ◽  
Face Tracking ◽  
Expression Recognition ◽  
Human Face ◽  
Eye Detection ◽  
Face Image Analysis ◽  
Face Images ◽  
The Face

Face detection is the most fundamental step for the research on image-based automated face analysis such as face tracking, face recognition, face authentication, facial expression recognition and facial gesture recognition. When a novel face image is given we must know where the face is located, and how large the scale is to limit our concern to the face patch in the image and normalize the scale and orientation of the face patch. Usually, the face detection results are not stable; the scale of the detected face rectangle can be larger or smaller than that of the real face in the image. Therefore, many researchers use eye detectors to obtain stable normalized face images. Because the eyes have salient patterns in the human face image, they can be located stably and used for face image normalization. The eye detection becomes more important when we want to apply model-based face image analysis approaches.

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 Unrestricted Face Recognition Algorithm Based on Transfer Learning on Self-Pickup Cabinet

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhixue Liang
Keyword(s):  
Face Recognition ◽  
Transfer Learning ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Face Image ◽  
Delivery Device ◽  
Face Images ◽  
Region Extraction ◽  
The Face ◽  
Enhancement Algorithm

In the contactless delivery scenario, the self-pickup cabinet is an important terminal delivery device, and face recognition is one of the efficient ways to achieve contactless access express delivery. In order to effectively recognize face images under unrestricted environments, an unrestricted face recognition algorithm based on transfer learning is proposed in this study. First, the region extraction network of the faster RCNN algorithm is improved to improve the recognition speed of the algorithm. Then, the first transfer learning is applied between the large ImageNet dataset and the face image dataset under restricted conditions. The second transfer learning is applied between face image under restricted conditions and unrestricted face image datasets. Finally, the unrestricted face image is processed by the image enhancement algorithm to increase its similarity with the restricted face image, so that the second transfer learning can be carried out effectively. Experimental results show that the proposed algorithm has better recognition rate and recognition speed on the CASIA-WebFace dataset, FLW dataset, and MegaFace dataset.

scholarly journals Gender Classification using Fisherface and Support Vector Machine on Face Image

2019 ◽  
Vol 1 (1) ◽  
pp. 32-40
Author(s):  
Muhammad Noor Fatkhannudin ◽  
Adhi Prahara
Keyword(s):  
Support Vector Machine ◽  
Principal Component ◽  
Face Image ◽  
Support Vector ◽  
Gender Classification ◽  
Biometric Recognition ◽  
Lighting Conditions ◽  
Face Images ◽  
The Face ◽  
Computer Vision Technology

Computer vision technology has been widely used in many applications and devices that involves biometric recognition. One of them is gender classification which has notable challenges when dealing with unique facial characteristics of human races. Not to mention the challenges from various poses of face and the lighting conditions. To perform gender classification, we resize and convert the face image into grayscale then extract its features using Fisherface. The features are reduced into 100 components using Principal Component Analysis (PCA) then classified into male and female category using linear Support Vector Machine (SVM). The test that conducted on 1014 face images from various human races resulted in 86% of accuracy using standard k-NN classifier while our proposed method shows better result with 88% of accuracy.

scholarly journals Evaluation of the DWT-PCA/SVD Recognition Algorithm on Reconstructed Frontal Face Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Louis Asiedu ◽  
Bernard O. Essah ◽  
Samuel Iddi ◽  
K. Doku-Amponsah ◽  
Felix O. Mettle
Keyword(s):  
Principal Component ◽  
Bilateral Symmetry ◽  
Recognition Algorithm ◽  
Face Image ◽  
Discrete Wavelet ◽  
Partial Occlusion ◽  
Face Images ◽  
Numerical Assessment ◽  
The Face ◽  
Value Decomposition

The face is the second most important biometric part of the human body, next to the finger print. Recognition of face image with partial occlusion (half image) is an intractable exercise as occlusions affect the performance of the recognition module. To this end, occluded images are sometimes reconstructed or completed with some imputation mechanism before recognition. This study assessed the performance of the principal component analysis and singular value decomposition algorithm using discrete wavelet transform (DWT-PCA/SVD) as preprocessing mechanism on the reconstructed face image database. The reconstruction of the half face images was done leveraging on the property of bilateral symmetry of frontal faces. Numerical assessment of the performance of the adopted recognition algorithm gave average recognition rates of 95% and 75% when left and right reconstructed face images were used for recognition, respectively. It was evident from the statistical assessment that the DWT-PCA/SVD algorithm gives relatively lower average recognition distance for the left reconstructed face images. DWT-PCA/SVD is therefore recommended as a suitable algorithm for recognizing face images under partial occlusion (half face images). The algorithm performs relatively better on left reconstructed face images.

Sign in / Sign up

Export Citation Format

Share Document