Clothes Size Prediction from Dressed-Human Silhouettes
We propose an effective and efficient way to automatically predict clothes size for users to buy clothes online. We take human height and dressed-human silhouettes in front and side views as input, and estimate 3D body sizes with a data-driven method. We adopt 20 body sizes which are closely related to clothes size, and use such 3D body sizes to get clothes size by searching corresponding size chart. Previous image-based methods need to calibrate camera to estimate 3D information from 2D images, because the same person has different appearances of silhouettes (e.g. size and shape) when the camera configuration (intrinsic and extrinsic parameters) is different. Our method avoids camera calibration, which is much more convenient. We set up our virtual camera and train the relationship between human height and silhouette size under this camera configuration. After estimating silhouette size, we regress the positions of 2D body landmarks. We define 2D body sizes as the distances between corresponding 2D body landmarks. Finally, we learn the relationship between 2D body sizes and 3D body sizes. The training samples for each regression process come from a database of 3D naked and dressed bodies created by previous work. We evaluate the whole procedure and each process of our framework. We also compare the performance with several regression models. The total time-consumption for clothes size prediction is less than 0.1 s and the average estimation error of body sizes is 0.824 cm, which can satisfy the tolerance for customers to shop clothes online.
KeywordsClothes size prediction Body size estimation Regression methods
The research is supported in part by NSFC (61572424) and the People Programme (Marie Curie Actions) of the European Unions Seventh Framework Programme FP7 (2007-2013) under REA grant agreement No. 612627-“AniNex”. Min Tang is supported in part by NSFC (61572423) and Zhejiang Provincial NSFC (LZ16F020003).
- 3.Lin, Y.L., Wang, M.J.J.: Automatic feature extraction from front and side images. In: IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008, pp. 1949–1953. IEEE (2008)Google Scholar
- 4.Nguyen, H.T.: Automatic anthropometric system development using machine learning. BRAIN Broad Res. Artif. Intell. Neurosci. 7(3), 5–15 (2016)Google Scholar
- 6.Dollar, P., Welinder, P., Perona, P.: Cascaded pose regression. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1078–1085 (2010)Google Scholar
- 9.Shao, H., Chen, S., Zhao, J., Cui, W., Yu, T.: Face recognition based on subset selection via metric learning on manifold. Front. Inf. Technol. Electron. Eng. 16(12), 1046–1058 (2015)Google Scholar
- 11.Liaw, A., Wiener, M.: Classification and regression by randomForest. R news 2(3), 18–22 (2002)Google Scholar
- 14.Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152 (1992)Google Scholar
- 15.Chen, Y., Cipolla, R.: Learning shape priors for single view reconstruction. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1425–1432. IEEE (2009)Google Scholar