Anthropometric measurements from images

Abstract

In this work, a system that simultaneously estimates several anthropometric measurements (namely, height and the circumferences of the bust, waist, and hip) using only two 2D images of a human subject has been proposed and tested. The proposed system has two components: a customized camera setup with four laser pointers and image analysis software. The camera setup includes an Android smartphone, four laser pointers around the smartphone's camera, and a tripod carrying the hardware. The image analysis software is a web-based application that has not been publicly available. The application takes the images as input, processes them, and yields the aforementioned anthropometric measurements in the unit of centimeters. The pipeline of the proposed system has the following components: 1. Feeding the images to the software, 2. Determining the locations of the body parts that will be measured, 3. Calculating the width of the body part on the specific location in both images (anterior and lateral), 4. Transforming pixel widths into physical units, 5. Estimating the circumference of the body part (or the height). For determining the locations of the body parts that will be measured, the software model applies pre-trained pose estimation and body segmentation models to both input images. For pose estimation, the MediaPipe framework, a tool developed for constructing pipelines based on sensory data, has been used. For body segmentation, BodyPix 2.0 in TensorFlow, a powerful tool that can perform whole-body segmentation on humans in real time, has been adopted. With the help of these models, body parts to be measured has been located on the input images. The width of a body part is measured as the largest distance between the left and right sides of the specific body part on the image. Laser points attached to the camera are leveraged while transforming pixel widths into physical units (i.e., centimeters). The last step of the measurements is converting the width into circumference. It is assumed that the cross-sectional areas of the body parts that are focused on in this research, namely, the bust, waist, and hip, are elliptical, and the circumferences of these body parts correspond to the perimeters of these ellipses. With the axes of the ellipses in hand, it is possible to estimate these anthropometric measurements. In order to evaluate the performance of the model, experiments were done on 19 volunteer human subjects. The actual measurements of these subjects were collected with traditional manual methods. The results obtained from the proposed model were compared with the actual measurements of the subjects, and the relative percentage errors were evaluated. The proposed hardware is a developed version of the prototype that was designed to assess the validity of the idea. The experiments described in this work, include the previous version of the proposed camera setup for better analysis and comparison. During the image collection stage of the experiment, the subjects that participated in the experiments are photographed with both versions of the camera setup, and the images are processed with software that is calibrated for individual camera setups. Finally, collected images are fed to a commercially available system that creates 3D meshes of humans from 2D images. This product can estimate body measurements from these meshes. For comparing the proposed system to a commercial product, this tool is included to the experiments. The images collected from the subjects who participated in the experiment are processed with the three systems mentioned earlier: the initial prototype, the improved version, and the commercially available tool. The results show that the initial prototype's relative errors for the bust, waist, and hip circumferences and height are 7.32%, 9.7%, 7.12%, and 5.0%, respectively. For the improved version, the errors become 15.97%, 9.92%, 2.01%, and 4.43%. The commercial product included in the study has relative errors of 7.8%, 10.69%, 12.43%, and 3.33% for the aforementioned body measurements. The main advantage of the proposed system over the alternative automatic methods is that, unlike the state-of-the-art measuring techniques, our method does not require predefined environmental conditions such as a specific background, a predetermined distance from the camera, or some clothing constraints. The lack of these restrictions makes the proposed system adaptable to various conditions, such as indoor and outdoor environments. The target user profile for this application would be medical practitioners, personal trainers, and individuals who want to keep track of their weight-loss progress since the system is lightweight, easy to use, and adaptable to various environments.