AdaBoost algorithm for the recognition of young women’s body shapesbased on 2D images

Classifying and recognizing the human body shape during human body measurements based on 2D images helps to improve measurement accuracy. In this paper, 430 young women's 2D body images were selected to establish 2D body datasets. The characteristic indices used to represent the body shape in 2D images were extracted by computer vision technology, namely the body height pixel value, projected unit area, and projected area ratio of the front and side of the body. The two-step cluster model was used to classify the body shape into three clusters: the tall, flat, and medium fatness type; the short, thin, and medium roundness type; and the round, fat, and medium height type. Then, the decision tree model and AdaBoost algorithm, an ensemble learning algorithm with the decision tree as the weak classifier, were used to recognize the body shape. The results show that the recognition accuracy of the decision tree recognition model was 93.19%. The body shape recognition method using AdaBoost achieved a better recognition effect than the decision tree model, and the recognition accuracy of the test set reached 97.35%. Through comparative study, we found that the recognition accuracy of the 2D body shape recognition method based on AdaBoost was improved and that the recognition accuracy was relatively stable. This study provides a new method for the recognition of human body shape in clothing customization and online shopping.