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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM55444

BMI Face Detection Using Deep Learning

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM55444

Volume:

9 |

12 |

Dec 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM55444

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

RB

Ruchitha B

MGW

Monika G Wali

PP

Prapthi P

SKF

S Kashfia Fathima

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Abstract

Body mass index (BMI) is a person’s weight in kilograms divided by the square of height in meters. Body mass index is a measurement of obesity based on measured height and weight. Traditional method of calculating BMI is inconvenient and requires physical measuring of a person and particular instruments. A proposed healthcare system to predict BMI using Kinect and data mining techniques so that everybody can easily predict their BMI values using Facial images. Face detection and feature extraction component using Haar cascade to detect useful face information. Framework by using facial images that uses machine learning algorithms for data mining namely, Data Preprocessing, Data Extraction, data evaluation and presentation to train models that would help predict obesity levels (Classification), Bodyweight, and fat percentage levels (Regression) using various parameters. System helps to advance the study aspect based on body weights and patients that are paralyzed or severely ill patient who unable to undergo basic measurement for emergency medical service.

KEYWORDS

Body Mass Index (BMI) Prediction Facial Image Processing Machine Learning Obesity Classification Kinect-Based Healthcare System

Cite This Article

Ruchitha B, Monika G Wali, Prapthi P, S Kashfia Fathima, "BMI Face Detection Using Deep Learning," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 12, Dec 2025. https://doi.org/10.55041/IJSREM55444

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Publication Date

12/25/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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