Quantitatively Plotting the Human Face for Multivariate Data Visualisation Illustrated by Health Assessments Using Laboratory Parameters

Objective. The purpose of this study was to describe a new data visualisation system by plotting the human face to observe the comprehensive effects of multivariate data.Methods. The Graphics Device Interface (GDI+) in the Visual Studio.NET development platform was used to write a program that enables facial image parameters to be recorded, such as cropping and rotation, and can generate a new facial image according toZvalues from sets of normal data (Z>3was still counted as 3). The measured clinical laboratory parameters related to health status were obtained from senile people, glaucoma patients, and fatty liver patients to illustrate the facial data visualisation system.Results. When the eyes, nose, and mouth were rotated around their own axes at the same angle, the deformation effects were similar. The deformation effects for any abnormality of the eyes, nose, or mouth should be slightly higher than those for simultaneous abnormalities. The facial changes in the populations with different health statuses were significant compared with a control population.Conclusions. The comprehensive effects of multivariate may not equal the sum of each variable. The 3Zfacial data visualisation system can effectively distinguish people with poor health status from healthy people.

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Objective assessment of stress levels and health status using routinely measured clinical laboratory parameters as biomarkers

Biomarkers ◽

10.3109/1354750x.2011.606505 ◽

2011 ◽

Vol 16 (6) ◽

pp. 525-529 ◽

Cited By ~ 7

Author(s):

Liu Hui ◽

Li Shijun ◽

Zhao Xinyu ◽

Wang Yuai ◽

Xu Xiaoting

Keyword(s):

Health Status ◽

Clinical Laboratory ◽

Objective Assessment ◽

Laboratory Parameters ◽

Stress Levels ◽

Assessment Of Stress

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Assessment of spinach seedling health status and chlorophyll content by multivariate data analysis and multiple linear regression of leaf image features

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2018.06.048 ◽

2018 ◽

Vol 152 ◽

pp. 281-289 ◽

Cited By ~ 5

Author(s):

Avinash Agarwal ◽

Snehasish Dutta Gupta

Keyword(s):

Data Analysis ◽

Health Status ◽

Linear Regression ◽

Multiple Linear Regression ◽

Chlorophyll Content ◽

Multivariate Data Analysis ◽

Multivariate Data ◽

Image Features ◽

Leaf Image

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Clinical laboratory parameters associated with severe or critical novel coronavirus disease 2019 (COVID-19): A systematic review and meta-analysis

PLoS ONE ◽

10.1371/journal.pone.0239802 ◽

2020 ◽

Vol 15 (10) ◽

pp. e0239802 ◽

Cited By ~ 1

Author(s):

Jude Moutchia ◽

Pratik Pokharel ◽

Aldiona Kerri ◽

Kaodi McGaw ◽

Shreeshti Uchai ◽

...

Keyword(s):

Systematic Review ◽

Clinical Laboratory ◽

Meta Analysis ◽

Laboratory Parameters ◽

Novel Coronavirus

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Hybrid Features Extraction for Adaptive Face Images Retrieval

International Journal of Synthetic Emotions ◽

10.4018/ijse.2020010102 ◽

2020 ◽

Vol 11 (1) ◽

pp. 17-26 ◽

Cited By ~ 1

Author(s):

Adel Alti

Keyword(s):

Feature Extraction ◽

Recognition Accuracy ◽

Face Image ◽

Human Face ◽

Gradient Vector ◽

Facial Image ◽

Hybrid Features ◽

Face Images ◽

Hybrid Feature Extraction ◽

Face Emotion Recognition

Existing methods of face emotion recognition have been limited in performance in terms of recognition accuracy and execution time. It is highly important to use efficient techniques for improving this performance. In this article, the authors present an automatic facial image retrieval combining the advantages of color normalization by texture estimators with the gradient vector. Starting from a query face image, an efficient algorithm for human face by hybrid feature extraction provides very interesting results.

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Effect of Seasonal Variation on Adult Clinical Laboratory Parameters in Rwanda, Zambia, and Uganda: Implications for HIV Biomedical Prevention Trials

PLoS ONE ◽

10.1371/journal.pone.0105089 ◽

2014 ◽

Vol 9 (8) ◽

pp. e105089 ◽

Cited By ~ 2

Author(s):

Eugene Ruzagira ◽

Andrew Abaasa ◽

Etienne Karita ◽

Joseph Mulenga ◽

William Kilembe ◽

...

Keyword(s):

Seasonal Variation ◽

Clinical Laboratory ◽

Laboratory Parameters ◽

Biomedical Prevention ◽

Prevention Trials

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Personalized quantification of facial normality: a machine learning approach

Scientific Reports ◽

10.1038/s41598-020-78180-x ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Osman Boyaci ◽

Erchin Serpedin ◽

Mitchell A. Stotland

Keyword(s):

Paradigm Shift ◽

Surgical Planning ◽

Outcome Measurement ◽

Great Promise ◽

Learning Approach ◽

Human Face ◽

Facial Image ◽

Perceptual Distance ◽

Machine Learning Approach ◽

Objective Tool

AbstractWhat is a normal face? A fundamental task for the facial reconstructive surgeon is to answer that question as it pertains to any given individual. Accordingly, it would be important to be able to place the facial appearance of a patient with congenital or acquired deformity numerically along their own continuum of normality, and to measure any surgical changes against such a personalized benchmark. This has not previously been possible. We have solved this problem by designing a computerized model that produces realistic, normalized versions of any given facial image, and objectively measures the perceptual distance between the raw and normalized facial image pair. The model is able to faithfully predict human scoring of facial normality. We believe this work represents a paradigm shift in the assessment of the human face, holding great promise for development as an objective tool for surgical planning, patient education, and as a means for clinical outcome measurement.

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