Automatic Clustering and Prediction of Female Breast Contours

The horizontal shape of breast is the key of shape categorization of female subjects. In this paper, Elliptic Fourier Analysis and two machine learning approaches (K-Means++ and Support Vector Machine) were used for the clustering and prediction of female breast. Female subjects were scanned by RGB-Depth camera (Microsoft Kinect). The breast contours and the under-breast contours were extracted via an anthropometric algorithm without manual intervention. Pearson Correlation Coefficient (PCC) was used to screen the breast candidate(s) for following shape clustering. Principal Component Analysis (PCA) was performed on the Elliptic Fourier Descriptors (EFDs), extracted during the Elliptic Fourier Analysis (EFA), followed by K-Means++ and SVM. K-Means++ was employed to determine the clustering number, meanwhile offered a credible labeled dataset for the subsequent Support Vector Machine (SVM). Finally, a prediction model was built through the SVM. The primary motivation for this research is to offer a quick reference tool for the designers of female bra. The proposed model was validated by reaching an accuracy of 90.5% for breast horizontal shape identification.