Analyzing Quetelet Index from Facial Features

Analyzing Quetelet Index from Facial Features

Author Name : Mrs. K Sri Devi, Dasari Lavanya, Guggilla Sreeneha, Kadicherla Tejaswi, Nune Chandra Kumari

ABSTRACT

The Quetelet Index also known as The Body Mass Index (BMI) serves as an indicator of a person's physical fitness in relation to their body weight. This measurement has been linked to various factors, including mental and physical health as well as societal status. Precise height and weight measurements are required for calculating QI, which can be a manual and time-consuming process. Streamlining the calculation of QI through automation would enable its use in the analysis of different societal aspects and support effective decision-making by governments and corporations. Previous studies have either focused solely on geometric facial features or employed data-driven deep learning approaches that are limited by the availability of data. We used the Convolution Neural Networks .Our study uses state-of-the-art pre-trained model, such as VGG16, which were fine-tuned on a large public dataset with discriminative learning. We trained our models on the extensive Arrest records dataset and evaluated their performance using different persons images.

Keywords: Convolution Neural Networks, VGG16, Body Mass Index, deep learning