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The 3D body scan technology has recently innovated the way of measuring human bodies and generated a large volume of body measurements. However, one inherent issue that plagues the use of the resultant database is the missing data usually caused by using automatic data extractions from the 3D body scans. Tedious extra efforts have to be made to man...
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... further examine the performance of the two ML methods with the proposed feature selection, box plots of the MAPE for predicting all the body features are shown in Figure 9. As seen, the minimum MAPE of the XGBoost and the RF is similar; however, the maximum MAPE of the XGBoost is around 14% while the maximum MAPE of the RF is 8%. ...
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... to Table 3, the mean MAPE of the RF is lower than the that of the XGBoost. The results in Figure 9 demonstrate that the RF has a better performance than the XGBoost when a combination of Bayesian search and feature selection is used. Table 4 shows the running time of the proposed framework for all body features. ...
Citations
... [2] focuses on the application of image processing techniques for human body measurement and virtual try-on of clothing; it presents algorithms and methods for extracting body measurements accurately from images and simulating the try-on experience virtually. [3] provides an overview of image processing techniques used for automatic human body measurement. It discusses various image analysis methods, feature extraction algorithms, and measurement estimation techniques employed in this field. ...
... These relationships were then used to predict and adjust the pattern parameters, achieving pattern adaptability. Liu et al. [16] proposed a machine learning framework that combines hybrid feature selection and a Bayesian search to estimate missing 3D body measurements, addressing the challenge of incomplete data in 3D body scanning. The study found that this approach leverages hybrid feature selection and the Bayesian search to enhance the performance of random forest (RF) and XGBoost 0.72, particularly in filling in missing data, where RF outperforms XGBoost. ...
With the increasing demand for intelligent custom clothing, the development of highly accurate human body dimension prediction tools using artificial neural network technology has become essential to ensuring high-quality, fashionable, and personalized clothing. Although support vector regression (SVR) networks have demonstrated state-of-the-art (SOTA) performances, they still fall short on prediction accuracy and computation efficiency. We propose a novel generalized regression forecasting network (GRFN) that incorporates kernel ridge regression (KRR) within a multi-strategy multi-subswarm particle swarm optimizer (MMPSO)-SVR nonlinear regression model that applies a residual correction prediction mechanism to enhance prediction accuracy for body dimensions. Importantly, the predictions are generated using only a few basic body size parameters from small-batch samples. The KRR regression model is employed for preliminary residual sequence prediction, and the MMPSO component optimizes the SVR parameters to ensure superior correction of nonlinear relations and noise data, thereby yielding more accurate residual correction value predictions. The GRFN hybrid model is superior to SOTA SVR models and increases the root mean square performance by 91.73–97.12% with a remarkably low mean square error of 0.0054 ± 0.07. This outstanding advancement sets the stage for marketable intelligent apparel design tools for the fast fashion industry.
... According to Jaeschke et al. [2], in order to improve the measurement of human body parameters (length, circumference of the trunk, hips, or other body parts), scanners visualizing a three-dimensional human model may prove useful. Liu et al. [13] stated that 3D scanners have fundamentally changed the approach to this type of anthropometric measurement in recent years. In [14], a synthetic data set of human body shapes was used to develop a method for estimating anthropometric parameters using deep learning and neural networks. ...
Anthropometric measurements of the human body are an important problem that affects many aspects of human life. However, anthropometric measurement often requires the application of an appropriate measurement procedure and the use of specialized, sometimes expensive measurement tools. Sometimes the measurement procedure is complicated, time-consuming, and requires properly trained personnel. This study aimed to develop a system for estimating human anthropometric parameters based on a three-dimensional scan of the complete body made with an inexpensive depth camera in the form of the Kinect v2 sensor. The research included 129 men aged 18 to 28. The developed system consists of a rotating platform, a depth sensor (Kinect v2), and a PC computer that was used to record 3D data, and to estimate individual anthropometric parameters. Experimental studies have shown that the precision of the proposed system for a significant part of the parameters is satisfactory. The largest error was found in the waist circumference parameter. The results obtained confirm that this method can be used in anthropometric measurements.
