Body Weight Estimation using 2D Body Image

Abstract

Two dimensional images of a person implicitly contain several useful biometric information such as gender, iris colour, weight, etc. Among them, body weight is a useful metric for a number of usecases such as forensics, fitness and health analysis, airport dynamic luggage allowance, etc. Most current solutions for body weight estimation from images make use of additional apparatus like depth sensors and thermal cameras along with predefined features such as gender and height which generally make them more computationally intensive. Motivated by the need to provide a time and cost efficient solution, a novel computer-vision based method for body weight estimation using only 2D images of people is proposed. Considering the anthropometric features from the two most common types of images, facial and full body, facial landmark measurements and body joint measurements are used in deep learning and XG boost regression models to estimate the person's body weight. The results obtained, though comparable to previous approaches, perform much faster due to the reduced complexities of the proposed models, with facial models performing better than full body models.

Identification of Fencing Athletes Based on Anthropometric Measurements Using MediaPipe Pose

“…The initial step that can be achieved from the MediaPipe is to use a camera or webcam. Next, the MediaPipe can display a 33-point skeleton, representing the human body [18]. Figure 1 displays the BlazePose, a lightweight machine learning architecture, that MPP uses to retrieve 33 2D landmarks from the human body [19].…”

“…To detect 2D landmarks from the human body on dataset, first step was to locate the pose region of interest (ROI) on image. Thirty-three landmark points were detected accurately in this measurement based on BlazePose [18]. However, as previously mentioned, only four points landmarks (S, E, I, and H) were used to classify the body pose image into two categories: fencing and nonfencing athletes.…”

Estimating Anthropometric Soft Biometrics: An Empirical Method

“…Like straight-line measurements, geometrical models are useful while estimating those circular or elliptical measurements of the human body. Such types of geometric measurements are genuinely useful anthropometric soft biometrics of the human body during recognition in the constrained or unconstrained scenario or feature-based retrieve from a large dataset of pedestrians [45]. In our work, we also identified several of these types of anthropometric soft biometrics of the human body as shown in Table 3, however, simultaneous images from more than one camera are required to estimate those soft biometrics from a 2D image.…”