Automatic body mass index detection using correlation of face visual cues

Bidani, Shiv; Priya, R.; Vijayarajan, V.; Prasath, V. B. Surya

doi:10.3233/thc-191850

Cited by 5 publications

(7 citation statements)

References 13 publications

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“…This paper is not concentrating more on establishing the relationship between the facial fiducial points and BMI. Because the previous studies [1,8,9,12,21] proved the existence of the relationship between the facial cues and the BMI; instead, this paper straight away goes to the prediction of BMI [22], by which, it convinces the objective of the study that the MLR model yields better results than the other methods. Hence, the facial features, such as geometrical and texture, extracted from each facial image [23,24] were straight away subject to the experiments using the MLR model discussed in Section 3.…”

Section: Resultsmentioning

confidence: 80%

Body Mass Index Prediction and Classification Based on Facial Morphological Cues Using Multinomial Logistic Regression

Maddumala¹,

Arunkumar²

2021

RIA

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This paper presents a novel method for body mass index prediction and classification based on the multinomial logistic regression model. The facial geometrical features are extracted and the logistic regression model parameters estimated based on the features. Based on the model parameters, the logistic model is fit in to predict the body mass index and classifies. Two different facial datasets are taken into account for the experiments. Each dataset is divided into two sets. One set is used to estimate the parameters while the other is used to fit-in the model and predicts the body mass index and classifies itself. The obtained outcome results show that the performance of the proposed method is comparable to the state-of-the-art techniques.

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Section: Resultsmentioning

confidence: 80%

Body Mass Index Prediction and Classification Based on Facial Morphological Cues Using Multinomial Logistic Regression

Maddumala¹,

Arunkumar²

2021

RIA

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“…This method can achieve better filtering effect, and it is clearer than only the image after neighborhood averaging. 17 During image acquisition, due to high temperature and other factors, the sensor will be affected by Gaussian noise, which needs to be suppressed by Gaussian filter. 18 The Gaussian filter is a linear filter, similar to the mean filter, that smoothes the image by suppressing noise.…”

Section: Image Preprocessing Of Double-column Metal Partsmentioning

confidence: 99%

“…The Canny edge detection operator has the best effect. 17 Canny is the optimal step-type edge detection operator. The specific implementation steps are as follows.…”

Section: Sub-pixel Precision Edge Extraction For Dual-column Metalmentioning

confidence: 99%

Application of subpixel edge detection in quality control of double‐column metal parts

Xie

Chen

2023

Adv Control Appl

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At present, most of the parts of mechanical equipment are made of metal, so the accurate detection and extraction of important characteristic parameters of metal parts is the key to determine the quality of mechanical equipment. In order to accurately extract and measure the feature size of metal parts, this research first carried out image preprocessing. Threshold segmentation is performed, and the target traits are extracted from the image to obtain the ROI. Finally, the preprocessed image is extracted from the image by the sub‐pixel edge extraction technology to extract the feature size of the circle to be measured. The difference between the measured value of the characteristic dimension of the spring bearing seat and the gasket and the real value measured by the research method is within the range of 5 and 4 μm, respectively, and both of them meet the requirements of the characteristic dimension accuracy. When the feature size of the two is repeatedly measured, the variation range of the detection results of the spring bearing seat and the gasket is within 9 and 7 μm respectively, and the detection value is much smaller than the feature size tolerance. The detection accuracy of the method can reach 95.394%. The score was 96.029 and the AUC value was 0.93, both higher than other methods. The results show that the use of sub‐pixels on metal parts The edge extraction method is extremely accurate. The research method can effectively improve the detection accuracy of the feature size of metal parts, which is of great significance to the development of the entire metal parts processing industry.

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“…The Body mass index of child was calculated by Kids health BMI for child from 1-2 years olds and percentage by weight (pound) and height (inches) (12) . The categories divided into: 1-Healthy (Normal) BMI equal to greater than 5th percentile and less than 85.…”

Section: Determination Of Body Mass Indexmentioning

confidence: 99%

Fibromodulin Level in Association with Clubfoot Disease and Congenital Dislocation of Hip in Children Patients in Najaf Province

Mohammed¹,

Al-Dujaili²

2022

The Egyptian Journal of Hospital Medicine

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Background:The main characterizations of foot deformity, which is called congenital talipes equinovarus, in severity and variation degree depend on complications and congenital malformations. They are classified into four main components such as midfoot cavus, forefoot adducts, heel/hind foot various and hind foot equines.Objective: This study aimed to estimate some of biochemical markers such as asporin, fibromodulin and tenascin-C in club foot child disease to determine pathological causes of severe deformity of bone, for monitoring complications of bone mineralization and calcification and for early treatment using drugs of choice. Patients and Methods: 60 Clubfoot patients with dislocation dysplasia of Hip (DDH) disease were included in this study. The samples were collected from Clubfoot Unit and private centers in Holy Najaf, Iraq during the period from December 2020 to February 2021. The patients groups were divided into subgroups according to age, gender, idiopathic, syndrome, Parina score, body mass index (BMI) and clubfoot. The control group included 30 apparent healthy participants and the age was nearly matched with patients' groups. Results:The level of tenascin-C was increased significantly in clubfoot patients than in control group. Also, tenascin-C level was highly increased in advanced ages (3-5) years in comparison with other ages and in males than in females. Also, high score (6-7) was significantly increased than other scores. The present results showed that club foot with dislocation dysplasia of hip was highly significant compared to clubfoot only and dislocation dysplasia of hip only in all children patients. Moreover, clubfoot syndrome was higher than idiopathic syndrome significantly whereas no differences in tenascin-C level in all subgroups according to BMI (normal, overweight and obese) of children of clubfoot. Conclusion: Increased levels of tenascin-C is very important as a pathological marker in patients of clubfoot with dysplasia of Hip.

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Automatic body mass index detection using correlation of face visual cues

Cited by 5 publications

References 13 publications

Body Mass Index Prediction and Classification Based on Facial Morphological Cues Using Multinomial Logistic Regression

Body Mass Index Prediction and Classification Based on Facial Morphological Cues Using Multinomial Logistic Regression

Application of subpixel edge detection in quality control of double‐column metal parts

Fibromodulin Level in Association with Clubfoot Disease and Congenital Dislocation of Hip in Children Patients in Najaf Province

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