Probabilistic Elastic Part Model for Unsupervised Face Detector Adaptation

2013 ◽  
Author(s):  
Haoxiang Li ◽  
Gang Hua ◽  
Zhe Lin ◽  
Jonathan Brandt ◽  
Jianchao Yang
Keyword(s):  
Elastic Part ◽  
Face Detector ◽  
Part Model

Miscibility and Crosslinking of Polybutadiene-Based Polyurethanes

2000 ◽  
Vol 65 (11) ◽  
pp. 1820-1832
Author(s):  
Miloslav Pekař ◽  
Pavel Kopecký
Keyword(s):  
Phase Structure ◽  
Initial Mixture ◽  
Cooperative Effect ◽  
Final Phase ◽  
Elastic Part ◽  
Hard Segments

Rheokinetics of polybutadiene-based polyurethanes was studied. Sixteen mixtures differing in the miscibility of reactive components and hard segments contents were prepared. Regardless of the miscibility of the components, the rheokinetics behaviour is qualitatively very similar. The viscous response part is formed and finished much earlier than the elastic part. The quantitative dissimilarities, caused by cooperative effect of miscibility and differences in reactivity, are described. Using a well miscible initial mixture need not give the best results as a reactive crosslinker can easily react with isocyanate and separate from the rest of the reaction mixture thus impairing the final phase structure.

scholarly journals SACN: A Novel Rotating Face Detector Based on Architecture Search

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 558
Author(s):  
Anping Song ◽  
Xiaokang Xu ◽  
Xinyi Zhai
Keyword(s):  
Face Detection ◽  
Human Face ◽  
Angle Error ◽  
Rotation Invariant ◽  
Convolutional Network ◽  
Data Set ◽  
Practical Applications ◽  
Model Size ◽  
Average Angle ◽  
Face Detector

Rotation-Invariant Face Detection (RIPD) has been widely used in practical applications; however, the problem of the adjusting of the rotation-in-plane (RIP) angle of the human face still remains. Recently, several methods based on neural networks have been proposed to solve the RIP angle problem. However, these methods have various limitations, including low detecting speed, model size, and detecting accuracy. To solve the aforementioned problems, we propose a new network, called the Searching Architecture Calibration Network (SACN), which utilizes architecture search, fully convolutional network (FCN) and bounding box center cluster (CC). SACN was tested on the challenging Multi-Oriented Face Detection Data Set and Benchmark (MOFDDB) and achieved a higher detecting accuracy and almost the same speed as existing detectors. Moreover, the average angle error is optimized from the current 12.6° to 10.5°.

scholarly journals Health and Housing Energy Expenditures: A Two-Part Model Approach

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 943
Author(s):  
Fátima Lima ◽  
Paula Ferreira ◽  
Vítor Leal
Keyword(s):  
Low Income ◽  
Health Expenditures ◽  
Household Level ◽  
Energy Expenditures ◽  
Health And Wellbeing ◽  
Health Gains ◽  
Key Factor ◽  
Using Data ◽  
Part Model ◽  
Model Approach

Interest in the interaction between energy and health within the built environment has been increasing in recent years, in the context of sustainable development. However, in order to promote health and wellbeing across all ages it is necessary to have a better understanding of the association between health and energy at household level. This study contributes to this debate by addressing the case of Portugal using data from the Household Budget Survey (HBS) microdata database. A two-part model is applied to estimate health expenditures based on energy-related expenditures, as well as socioeconomic variables. Additional statistical methods are used to enhance the perception of relevant predictors for health expenditures. Our findings suggest that given the high significance and coefficient value, energy expenditure is a relevant explanatory variable for health expenditures. This result is further validated by a dominance analysis ranking. Moreover, the results show that health gains and medical cost reductions can be a key factor to consider on the assessment of the economic viability of energy efficiency projects in buildings. This is particularly relevant for the older and low-income segments of the population.

scholarly journals Association between fasting insulin and C-reactive protein among adults without diabetes using a two-part model: NHANES 2005–2010

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Amanda L. Missel ◽  
Laura R. Saslow ◽  
Dina H. Griauzde ◽  
Donna Marvicsin ◽  
Ananda Sen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Waist Circumference ◽  
Low Grade ◽  
Fasting Insulin ◽  
C Reactive Protein ◽  
Glucose Levels ◽  
Reactive Protein ◽  
Part Model

Abstract Introduction Chronic inflammation is associated with the development, progression and long-term complications of type 2 diabetes. Hyperglycemia is associated with chronic low-grade inflammation, and thus has become the focus of many screening and treatment recommendations. We hypothesize that insulin may also be associated with inflammation and may be an additional factor to consider in screening and treatment. Methods This study used National Health and Nutrition Examination Survey data from 2005 to 2010 to analyze the association between fasting insulin and C-reactive protein (CRP). A two-part model was used due to the high number of values reported as 0.1 mg/L. Two models were analyzed, both with and without the addition of waist circumference to other covariates in the model. Results The final sample included 4527 adults with a mean age of 43.31 years. In the first model, higher fasting insulin was associated with increased odds of CRP > 0.1 mg/L (OR = 1.02, p < .001) and with higher CRP (β = 0.03, p < .001). In the adjusted model, including waist circumference as a covariate, higher fasting insulin was not associated with CRP > 0.1 mg/L (OR = 1.00, p = .307) but the association between higher fasting insulin and higher continuous CRP remained significant (β = 0.01, p = .012). Conclusion This study found that higher fasting insulin is associated with higher CRP. These results suggest that treatment approaches that simultaneously decrease insulin levels as well as glucose levels may provide additive anti-inflammatory effects, and therefore may improve long-term outcomes for adults with type 2 diabetes.

scholarly journals What does it mean when we ask the public if they are ‘confident’ in policing? The trust, fairness, presence model of ‘public confidence’

2019 ◽  
Vol 22 (2) ◽  
pp. 111-122 ◽  
Author(s):  
Kevin Morrell ◽  
Ben Bradford ◽  
Basit Javid
Keyword(s):  
Survey Data ◽  
Good Evidence ◽  
Fundamental Question ◽  
Police Force ◽  
Public Confidence ◽  
Single Item ◽  
The Public ◽  
West Midlands ◽  
The Relationship ◽  
Part Model

‘Confidence’ is widely taken to be a crucial measure of the relationship between citizens and public services such as policing. It is acknowledged that confidence is multifaceted and hard to measure, but often discussions are based on one ‘headline’ rating of a single item, for instance: ‘What is your level of confidence in…’. The subsequent focus for research is explaining what might drive ‘confidence’, or what it might predict. We are interested in a more fundamental question: what does it mean when we ask the public if they are ‘confident’ in policing? To answer this, we analyse extensive and detailed survey data specifically designed to measure public confidence, within the jurisdiction of a UK police force – West Midlands Police. We develop then validate a three-part model of confidence as trust, fairness and presence, and find good evidence to support this model across different waves of the survey. This extends existing literature with implications for policy.

scholarly journals Facial Recognition System for People with and without Face Mask in Times of the COVID-19 Pandemic

2021 ◽  
Vol 13 (12) ◽  
pp. 6900
Author(s):  
Jonathan S. Talahua ◽  
Jorge Buele ◽  
P. Calvopiña ◽  
José Varela-Aldás
Keyword(s):  
Facial Recognition ◽  
Face Mask ◽  
Recognition System ◽  
Classification Model ◽  
World Health ◽  
The Face ◽  
Recognition Systems ◽  
Facial Recognition System ◽  
Health Organization ◽  
Face Detector

In the face of the COVID-19 pandemic, the World Health Organization (WHO) declared the use of a face mask as a mandatory biosafety measure. This has caused problems in current facial recognition systems, motivating the development of this research. This manuscript describes the development of a system for recognizing people, even when they are using a face mask, from photographs. A classification model based on the MobileNetV2 architecture and the OpenCv’s face detector is used. Thus, using these stages, it can be identified where the face is and it can be determined whether or not it is wearing a face mask. The FaceNet model is used as a feature extractor and a feedforward multilayer perceptron to perform facial recognition. For training the facial recognition models, a set of observations made up of 13,359 images is generated; 52.9% images with a face mask and 47.1% images without a face mask. The experimental results show that there is an accuracy of 99.65% in determining whether a person is wearing a mask or not. An accuracy of 99.52% is achieved in the facial recognition of 10 people with masks, while for facial recognition without masks, an accuracy of 99.96% is obtained.

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