Age Regression with Specific Facial Landmarks by Dual Discriminator Adversarial Autoencoder

2021 ◽  
Author(s):  
Li-Chi Lan ◽  
Tsung-Jung Liu ◽  
Kuan-Hsien Liu
Keyword(s):  
Facial Landmarks ◽  
Age Regression

Exploring the Facial Color Representative Regions Using the Humanae Images

2020 ◽  
Author(s):  
Yuchun Yan ◽  
Hayan Choi ◽  
Hyeon-Jeong Suk
Keyword(s):  
Image Analysis ◽  
Future Study ◽  
Skin Color ◽  
Color Difference ◽  
Facial Skin ◽  
Female Images ◽  
Gender And Race ◽  
Facial Landmarks

It is difficult to describe facial skin color through a solid color as it varies from region to region. In this article, the authors utilized image analysis to identify the facial color representative region. A total of 1052 female images from Humanae project were selected as a solid color was generated for each image as their representative skin colors by the photographer. Using the open CV-based libraries, such as EOS of Surrey Face Models and DeepFace, 3448 facial landmarks together with gender and race information were detected. For an illustrative and intuitive analysis, they then re-defined 27 visually important sub-regions to cluster the landmarks. The 27 sub-region colors for each image were finally derived and recorded in L ∗ , a ∗ , and b ∗ . By estimating the color difference among representative color and 27 sub-regions, we discovered that sub-regions of below lips (low Labial) and central cheeks (upper Buccal) were the most representative regions across four major ethnicity groups. In future study, the methodology is expected to be applied for more image sources.

Driver’s Drowsiness Detection Using Dlib and IOT

2020 ◽  
Vol 10 ◽  
Author(s):  
Prasanna Lakshmi Kompalli ◽  
Padma Vallakati ◽  
Ganapathi Raju Nadimpalli ◽  
Vinod Mahesh Jain ◽  
Samuel Annepogu
Keyword(s):  
Aspect Ratio ◽  
The Other ◽  
Human Intervention ◽  
Road Accidents ◽  
Online Content ◽  
Facial Landmarks ◽  
Drowsiness Detection ◽  
Rear Part ◽  
Cause Of Deaths ◽  
Published Paper

Background: Road accidents are major cause of deaths worldwide. This is enormously due to fatigue, drowsiness and microsleep of the drivers. This don’t just risk the life of driver and copassengers but also a great threat to the vehicles and humans moving around that vehicle. Methods: Research, online content and previously published paper related to drowsiness are reviewed. Using the facial landmarks DAT file, the prototype will locate and get the eye coordinates and it will calculate Eye Aspect Ratio (EAR). The EAR indicates whether the driver is drowsy or not based on the result various sensors gets activated such as Alarm generator, LED Indicators, LCD message scroll, message sent to owner and engine gets locked. Results: The prototype is able to locate eyes in the frame and detect whether the person is sleepy or not. Whenever the person is feeling drowsy alarm gets generated in the cabinet on further if the person is feeling drowsy, LED indicators will start glowing, messaging will be scrolling at the rear part of vehicle so that other vehicles and humans gets cautioned and vehicle slows down and engine gets locked. Conclusion: This prototype will help in reduction of road accidents due to human intervention. It is not only helpful to the person who install it in their vehicles but also for the other vehicles and humans moving around it.

scholarly journals Understanding the unimodal distributions of cancer occurrence rates: it takes two factors for a cancer to occur

2020 ◽  
Author(s):  
Shuang Qiu ◽  
Zheng An ◽  
Renbo Tan ◽  
Ping-an He ◽  
Jingjing Jing ◽  
...  
Keyword(s):  
Occurrence Rate ◽  
Cancer Drug ◽  
Proliferative Potential ◽  
Cancer Type ◽  
Cancer Occurrence ◽  
Important Relationship ◽  
Age Regression ◽  
Age Dependent ◽  
Two Factors ◽  
Realistic Possibility

Abstract Data from the SEER reports reveal that the occurrence rate of a cancer type generally follows a unimodal distribution over age, peaking at an age that is cancer-type specific and ranges from 30+ through 70+. Previous studies attribute such bell-shaped distributions to the reduced proliferative potential in senior years but fail to explain why some cancers have their occurrence peak at 30+ or 40+. We present a computational model to offer a new explanation to such distributions. The model uses two factors to explain the observed age-dependent cancer occurrence rates: cancer risk of an organ and the availability level of the growth signals in circulation needed by a cancer type, with the former increasing and the latter decreasing with age. Regression analyses were conducted of known occurrence rates against such factors for triple negative breast cancer, testicular cancer and cervical cancer; and all achieved highly tight fitting results, which were also consistent with clinical, gene-expression and cancer-drug data. These reveal a fundamentally important relationship: while cancer is driven by endogenous stressors, it requires sufficient levels of exogenous growth signals to happen, hence suggesting the realistic possibility for treating cancer via cleaning out the growth signals in circulation needed by a cancer.

scholarly journals Guided Spatial Transformers for Facial Expression Recognition

2021 ◽  
Vol 11 (16) ◽  
pp. 7217
Author(s):  
Cristina Luna-Jiménez ◽  
Jorge Cristóbal-Martín ◽  
Ricardo Kleinlein ◽  
Manuel Gil-Martín ◽  
José M. Moya ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expert Knowledge ◽  
Visual Saliency ◽  
Expression Recognition ◽  
Spatial Transformations ◽  
Facial Landmarks ◽  
Saliency Maps ◽  
Best Fit

Spatial Transformer Networks are considered a powerful algorithm to learn the main areas of an image, but still, they could be more efficient by receiving images with embedded expert knowledge. This paper aims to improve the performance of conventional Spatial Transformers when applied to Facial Expression Recognition. Based on the Spatial Transformers’ capacity of spatial manipulation within networks, we propose different extensions to these models where effective attentional regions are captured employing facial landmarks or facial visual saliency maps. This specific attentional information is then hardcoded to guide the Spatial Transformers to learn the spatial transformations that best fit the proposed regions for better recognition results. For this study, we use two datasets: AffectNet and FER-2013. For AffectNet, we achieve a 0.35% point absolute improvement relative to the traditional Spatial Transformer, whereas for FER-2013, our solution gets an increase of 1.49% when models are fine-tuned with the Affectnet pre-trained weights.

scholarly journals Vermal Length and Transcerebellar Diameter in Gestational Age Estimation: A Guide to Forensic Expert

2019 ◽  
Vol 08 (03) ◽  
pp. 101-105
Author(s):  
Nadia Ahmad ◽  
S. L. Jethani ◽  
Deepa Singh ◽  
Ruchira Nautiyal
Keyword(s):  
Gestational Age ◽  
Age Determination ◽  
Linear Increase ◽  
Group 4 ◽  
Age Regression ◽  
Group 3 ◽  
Group 2 ◽  
Gestational Age Estimation ◽  
Formalin Fixed ◽  
Group 1

Abstract Background Transcerebellar diameter is one of the reliable, constant predicting parameters to assess the gestational age and fetal growth. Other than this, measurements of vermis, mostly the vermal length (height), have also been mentioned by authors to assess gestational age. Establishing a correlation between parameters and advancing gestation would be helpful in estimating the gestational age of fetus. Aims and Objectives To establish a correlation of vermal length and transcerebellar diameter with gestational age. Materials and Methods An observational and descriptive study conducted on 60 formalin-fixed human cerebellums. Fetuses with gross congenital/neurological abnormality were excluded. Fetuses were grouped into four groups—group 1 (13–17 weeks), group 2 (18–22 weeks), group 3 (23–27 weeks), and group 4 (28–32 weeks of gestation). Vermal length and transcerebellar diameter were measured with help of Vernier calipers. The data obtained were analyzed using statistical software SPSS version 20.0 and one-way analysis of variance. Observation A linear increase in vermal length parameters and transcerebellar diameter were seen with increasing gestational age. Regression analysis was done and regression equation was derived for each parameter. Conclusion Such correlations would help in fetal age determination in the field of forensic studies.

The Development of an Identification Photo Booth System based on a Deep Learning Automatic Image Capturing Method

2020 ◽  
Author(s):  
Yu-Xiang Zhao ◽  
Yi-Zeng Hsieh ◽  
Shih-Syun Lin
Keyword(s):  
Deep Learning ◽  
Experimental Results ◽  
Automatic Annotation ◽  
Learning Method ◽  
Facial Region ◽  
Facial Landmarks ◽  
Image Capturing ◽  
The Face ◽  
Facial Contours

With advances in technology, photo booths equipped with automatic capturing systems have gradually replaced the identification (ID) photo service provided by photography studios, thereby enabling consumers to save a considerable amount of time and money. Common automatic capturing systems employ text and voice instructions to guide users in capturing their ID photos; however, the capturing results may not conform to ID photo specifications. To address this issue, this study proposes an ID photo capturing algorithm that can automatically detect facial contours and adjust the size of captured images. The authors adopted a deep learning method (You Only Look Once) to detect the face and applied a semi-automatic annotation technique of facial landmarks to find the lip and chin regions from the facial region. In the experiments, subjects were seated at various distances and heights for testing the performance of the proposed algorithm. The experimental results show that the proposed algorithm can effectively and accurately capture ID photos that satisfy the required specifications.

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