Stereoscopic Face Reconstruction from a Single 2-Dimensional Face Image Using Orthogonality of Normal Surface and Y-Ratio

2016 ◽  
Vol 30 (02) ◽  
pp. 1655006 ◽  
Author(s):  
Natchamol Srichumroenrattana ◽  
Rajalida Lipikorn ◽  
Chidchanok Lursinsap
Keyword(s):  
Face Image ◽  
Normal Surface ◽  
Height Estimation ◽  
3 Dimensional ◽  
Face Reconstruction ◽  
Face Images ◽  
Facial Height ◽  
Input Face ◽  
Actual Height ◽  
Dimensional Face

This paper modified the method of three-dimensional (3-dimensional) face reconstruction from a single two-dimensional (2-dimensional) image based on the Lambertian model consisting of height estimation, normal surface estimation, albedo calculation and image normalization, normal surface calculation, actual height calculation, and error correction. In height estimation, the facial height of each input image is estimated from the average facial height of face images in the training data set. The estimated height is used for estimating the normal surface by applying our proposed pattern morphing method (PMM). To calculate the actual normal surface, the albedo of the input face image is calculated to normalize the image first. Then, the actual normal surface is derived by using our proposed Y-ratio calculation with improved computational time. Finally, the actual height of input face image is computed afterwards to construct the 3-dimensional face. Two face databases of 110 human face images containing 10 real and 100 synthetic images were tested by our proposed method with uniform reflectance and high level of heterogeneous reflectance abilities. The experimental results were compared with the results obtained from other existing methods, such as the traditional minimization, shape propagation, local, and linear approaches. Our method can accurately reconstruct 3-dimensional face from a single 2-dimensional face image with the error less than 6%.

scholarly journals Correlative microscopy and block-face imaging (CoMBI) method for both paraffin-embedded and frozen specimens

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nobukazu Ishii ◽  
Yuki Tajika ◽  
Tohru Murakami ◽  
Josephine Galipon ◽  
Hiroyoshi Shirahata ◽  
...  
Keyword(s):  
Single Neuron ◽  
Bile Canaliculus ◽  
Face Image ◽  
Correlative Microscopy ◽  
Preparation Methods ◽  
Microscopic Images ◽  
3 Dimensional ◽  
Face Images ◽  
Block Face ◽  
New System

AbstractCorrelative microscopy and block-face imaging (CoMBI), a method that we previously developed, is characterized by the ability to correlate between serial block-face images as 3-dimensional (3D) datasets and sections as 2-dimensional (2D) microscopic images. CoMBI has been performed for the morphological analyses of various biological specimens, and its use is expanding. However, the conventional CoMBI system utilizes a cryostat, which limits its compatibility to only frozen blocks and the resolution of the block-face image. We developed a new CoMBI system that can be applied to not only frozen blocks but also paraffin blocks, and it has an improved magnification for block-face imaging. The new system, called CoMBI-S, comprises sliding-type sectioning devices and imaging devices, and it conducts block slicing and block-face imaging automatically. Sections can also be collected and processed for microscopy as required. We also developed sample preparation methods for improving the qualities of the block-face images and 3D rendered volumes. We successfully obtained correlative 3D datasets and 2D microscopic images of zebrafish, mice, and fruit flies, which were paraffin-embedded or frozen. In addition, the 3D datasets at the highest magnification could depict a single neuron and bile canaliculus.

scholarly journals Real Time Face Recognition using Eigenface and Viola-Jones Face Detector

2017 ◽  
Vol 1 (1) ◽  
pp. 16 ◽  
Author(s):  
Jacky Efendi ◽  
Muhammad Ihsan Zul ◽  
Wawan Yunanto
Keyword(s):  
Face Recognition ◽  
Light Intensity ◽  
Real Time ◽  
Face Image ◽  
Recognition Time ◽  
Final Project ◽  
Face Images ◽  
Input Face ◽  
The Subject ◽  
Face Detector

Authentication is the process of verifying one’s identity, and one of its implementation is in taking attendances in university’s lectures. Attendance taking is a very important matter to every academic institution as a way to examine students’ performance. Signature based attendance taking can be manipulated. Therefore it has problems in verifying the attendance validity. In this final project, a real time eigenface based face recognition is implemented in an application to do attendance taking. The input face image is captured using a webcam. The application itself is built in C#, utilizing EmguCV library. The application is developed using Visual Studio 2015. Face detection is done with Viola-Jones algorithm. The eigenface method is used to do facial recognition on the detected face image. In this final project, a total of 8 testings are done in different conditions. From the testings, it is found that this application can recognize face images with accuracy as high as 90% and as low as 6.67%. This solution can be used as an alternative for real-time attendance taking in an environment with 170 lux light intensity, webcam resolution of 320 x 240 pixel, and the subject standing 1 meter away while not wearing spectacles. The average recognition time is 0.18125 ms.

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.

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 Choroidal vessel density in unilateral hyperopic amblyopia using en-face optical coherence tomography

2020 ◽  
Author(s):  
Syunsuke Araki ◽  
Atsushi Miki ◽  
Katsutoshi Goto ◽  
Tsutomu Yamashita ◽  
Tsuyoshi Yoneda ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Normal Control ◽  
Structural Changes ◽  
Vascular Density ◽  
Optical Coherence ◽  
Swept Source ◽  
3 Dimensional ◽  
Face Images ◽  
En Face ◽  
Normal Controls

Abstract Background Structural changes of the choroid, such as choroidal thickening, have been indicated in amblyopic eyes with hyperopic anisometropia as compared to fellow or healthy eyes. The purpose of the present study was to investigate choroidal vascular density (CVD) in children with unilateral hyperopic amblyopia.Methods This study included 88 eyes of 44 patients with unilateral amblyopia due to hyperopic anisometropia with or without strabismus and 29 eyes of 29 age-matched normal controls. The CVD of Haller's layer was quantified from en-face images constructed by 3-dimensional swept-source optical coherence tomography images flattened relative to Bruch's membrane. The analysis area was a 3×3-mm square of macula after magnification correction. Relationships between CVD and other parameters [best-corrected visual acuity (BCVA), refractive error and subfoveal choroidal thickness (SFCT)] were investigated, and CVDs were compared between amblyopic, fellow, and normal control eyes.Results Mean CVD was 59.11 ± 0.66% in amblyopic eyes, 59.23 ± 0.81% in fellow eyes, and 59.29 ± 0.74% in normal control eyes. CVD showed a significant positive relationship with SFCT (p=0.004), but no relationships with other parameters. No significant differences in CVD were evident among amblyopic, fellow, and normal control eyes after adjusting for SFCT (p=0.502).Conclusions CVD was unrelated to BCVA, and CVD did not differ significantly among amblyopic, fellow and normal control eyes. These results suggest that the local CVD of Haller's layer is unaffected in unilateral hyperopic amblyopic eyes.

scholarly journals Improvements on Learning Kernel Extended Dictionary for Face Recognition

2020 ◽  
Vol 34 (4) ◽  
pp. 387-394
Author(s):  
Soodabeh Amanzadeh ◽  
Yahya Forghani ◽  
Javad Mahdavi Chabok
Keyword(s):  
Sparse Representation ◽  
Face Image ◽  
Collaborative Representation ◽  
Training Images ◽  
Input Face ◽  
Proposed Model ◽  
Eigen Values ◽  
New Type ◽  
The Mean ◽  
Better Than

Kernel extended dictionary learning model (KED) is a new type of Sparse Representation for Classification (SRC), which represents the input face image as a linear combination of dictionary set and extended dictionary set to determine the input face image class label. Extended dictionary is created based on the differences between the occluded images and non-occluded training images. There are four defaults to make about KED: (1) Similar weights are assigned to the principle components of occlusion variations in KED model, while the principle components of the occlusion variations have different weights, which are proportional to the principle components Eigen-values. (2) Reconstruction of an occluded image is not possible by combining only non-occluded images and the principle components (or the directions) of occlusion variations, but it requires the mean of occlusion variations. (3) The importance and capability of main dictionary and extended dictionary in reconstructing the input face image is not the same, necessarily. (4) KED Runtime is high. To address these problems or challenges, a novel mathematical model is proposed in this paper. In the proposed model, different weights are assigned to the principle components of occlusion variations; different weights are assigned to the main dictionary and extended dictionary; an occluded image is reconstructed by non-occluded images and the principle components of occlusion variations, and also the mean of occlusion variations; and collaborative representation is used instead of sparse representation to enhance the runtime. Experimental results on CAS-PEAL subsets showed that the runtime and accuracy of the proposed model is about 1% better than that of KED.

scholarly journals RoboCoDraw: Robotic Avatar Drawing with GAN-Based Style Transfer and Time-Efficient Path Optimization

2020 ◽  
Vol 34 (06) ◽  
pp. 10402-10409
Author(s):  
Tianying Wang ◽  
Wei Qi Toh ◽  
Hao Zhang ◽  
Xiuchao Sui ◽  
Shaohua Li ◽  
...  
Keyword(s):  
Face Image ◽  
Path Optimization ◽  
Robotic Arm ◽  
Human Face ◽  
Generative Adversarial Network ◽  
Style Transfer ◽  
Adversarial Network ◽  
Face Images ◽  
Drawing System ◽  
Collaborative Robot

Robotic drawing has become increasingly popular as an entertainment and interactive tool. In this paper we present RoboCoDraw, a real-time collaborative robot-based drawing system that draws stylized human face sketches interactively in front of human users, by using the Generative Adversarial Network (GAN)-based style transfer and a Random-Key Genetic Algorithm (RKGA)-based path optimization. The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm. A core component in this system is the AvatarGAN proposed by us, which generates a cartoon avatar face image from a real human face. AvatarGAN is trained with unpaired face and avatar images only and can generate avatar images of much better likeness with human face images in comparison with the vanilla CycleGAN. After the avatar image is generated, it is fed to a line extraction algorithm and converted to sketches. An RKGA-based path optimization algorithm is applied to find a time-efficient robotic drawing path to be executed by the robotic arm. We demonstrate the capability of RoboCoDraw on various face images using a lightweight, safe collaborative robot UR5.

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.

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