Extraction and recognition of breast morphological feature parameters based on machine learning models

Breast feature parameters could represent breast morphology. It is significant for improving bra fit, and is an important aspect of garment ergonomics. To obtain the important feature parameters that can effectively represent breast morphology, this study proposed a feature parameter extraction method based on the machine learning model. First, the human body point-cloud data of 201 female college students were obtained by a three-dimensional body scanner, and 24 feature parameters related to breast morphology were acquired. Then, the cluster analysis was used to classify breast morphology into four categories: uniform hemisphere, outward expanding circular, converging water drop, and outward expanding hemisphere. Finally, principal component analysis was used to reduce the dimensionality of feature parameters, and the three machine learning models, naive Bayes, support vector machine, and random forest, were utilized to extract the parameters after dimensionality reduction. The results showed that principal component analysis could reduce the dimensions of breast feature parameters to seven main parameters. Based on the above three models, the seven main parameters were further reduced to three important feature parameters. They were sorted sequentially: breast volume, breast surface area, and longitudinal breast cup straight line length, and the Fisher discriminate function was used to distinguish breast morphology. The recognition accuracy based on the three important feature parameters reached 99%, higher than 97.5% for full feature parameters recognition, and 98% for seven feature parameters recognition. It is proved that the three important feature parameters obtained by the machine model are effective in characterizing breast morphology.