# Ensemble Learning for Skeleton-Based Body Mass Index Classification
## 2 Related Work  
Previous studies on BMI classification have mainly focused on calculating human weight or BMI from facial images [8; 9; 10; 29]. For example, [PERSON] et al. [8] demonstrated that three facial features, namely the width-to-height ratio, perimeter-to-area ratio and cheek-to-jaw-width ratio, are highly related to both human weight and BMI. Additionally, they proposed the use of these three features to judge the body weight and BMI of an individual. Motivated by this study, [PERSON] et al. [9] assessed the relationship between these facial features and BMIs for facial images of 911 subjects. They demonstrated that the eye size and average eyebrow height are also related to the BMI. [PERSON] and [PERSON] [10] investigated a method for predicting the BMIs of people from face images. To achieve this goal, they extracted the facial features proposed in [8; 9] from passport-style frontal face images with clean backgrounds. They then trained a support vector regression (SVR) model using these features and demonstrated the feasibility of using SVR to predict BMIs. [PERSON] et al. [29] explored a method for predicting the BMI using low-quality facial images collected from social media. To achieve this goal, they used the convolutional neural network (CNN) proposed in [30]. They then used the outputs from the sixth fully connected layer of the CNN as features for the BMI prediction. They trained an SVR model using these features and evaluated their model using a publicly available social media  
Figure 1: Block diagram for classifying the body mass index (BMI) from 3D human motion sequence. face dataset. The evaluation results demonstrated the feasibility of BMI prediction using low-quality facial images. BMI prediction/classification based on full-body person images has also been studied [11; 12; 13]. For example, [PERSON] et al. [11] proposed a method for obtaining the BMI of an individual from their full-body silhouette image. To achieve this goal, the authors captured frontal full-body images of standing individuals. They then converted these images into silhouette images. Based on a silhouette image, they calculated the area and height of an individual. The area was defined as the number of pixels occupied by a silhouette, and the height was defined as the distance between the highest and lowest pixels in a silhouette. Finally, the BMI was calculated using the extracted values of the area and height. [PERSON] et al. [12] also used human silhouette images for BMI classification. A total of 57 shape features introduced in [31] were extracted from images. Four machine learning algorithms, namely logistic regression, a Bayesian classifier, an artificial neural network and support vector machine (SVM), were tested on their own dataset consisting of 122 subjects. According to their results, the SVM outperformed the other algorithms and achieved an accuracy of approximately 72.16%. [PERSON] et al. [13] proposed the calculation of human height from full-body images using the vanishing point and camera height information. For body weight calculation, a sensor called the Wheatstone bridge load cell was used. Using the estimated height and measured body weight information, the BMI was calculated. [PERSON] et al. [32] investigated a method for estimating BMIs from depth images of people. To achieve this goal, the deep residual network (ResNet) proposed in [33] was used. To train the ResNet, 3D human polygon models were generated using the MakeHuman open-source software. The body surface area (BSA) of each model was calculated. By using the formula BSA = \(1/6(\text{body weight}\times\text{height})^{0.5}\)[34], the body weight of each model was estimated. For each polygon model, a BMI was then obtained using the height and estimated body weight. These were then used as ground truth BMI values to train the ResNet. Additionally, 3D depth images were obtained from 3D human polygon models using a rendering software and encoded using red-green-blue (RGB) colorization.