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Automatic recognition of the neck–shoulder shape based on 2D photos

To improve body type classification and automatic identification, this study adopted a method by combining three-dimensional (3D) point-cloud data with two-dimensional (2D) photos for the classification of young mens’ neck–shoulder shape. A total of 180 male college students were measured by photo and 3D body measuring methods to get the main body information. As for the 3D body cloud data, reverse engineering software was used to measure the parameters closely related to the neck–shoulder shape. Five important parameters, including the forward angle, back angle, shoulder angle, the ratio between neck and shoulder width, and the ratio between neck width and thickness were analyzed to divide young men’s neck–shoulder shape into three categories, namely round drop shoulder, forward round neck and wide straight neck, and the classification rules were established. As for the 2D photos (such as front and side photos), the human body contour was extracted to obtain the parameters required for neck–shoulder shape classification by identifying the feature points. The neck–shoulder shape could be automatically recognized, and the accuracy rate is 97.7% to verify that the neck–shoulder shape recognition system is effective. This study proposed a simple neck–shoulder shape automatic recognition method based on the 2D body photos, and provide reference for automatic neck–shoulder shape recognition, 3D modeling and virtual fitting of local features.