## 2 Related Research  
### Size Extraction System  
In a study by [PERSON] and [PERSON] (2010) [9] on \"Extraction of full body size parameters for personalized recommendation module\" and\"Intelligent code recommendation system using body size\". It is a method of product recommendation through quick body area detection and AAM (active appearance model) using Haar-like features and the AdaBoost algorithm. The research process is as follows in Figure 1[9]. The study extracted more complicated customer numerical values. The process involved photo-taking and the recommended data were not real-time sold products but limited to existing classic products. The study below was implemented to reflect user convenience. Customers were not required to take a photo, and only had to input height and weight. Moreover, the product provider made recommendations with an internal comparison system by codifying the size in the product information. Therefore, it is convenient for use in ordinary internet shopping malls. Customers merely input height and weight without complicated operation in existing shopping malls and can receive the most suitable size of the product. [PERSON] and [PERSON] (2016) proposed [10] a method of recommending different sizes for the women's clothing by using a machine-learning technique based on the feature data of body types in different age groups to focus on solving the problem of changing clothing sizes. First, the data of body types of 300 women aged between 18 and 50 were selected randomly from an experimental database where the body features included the lengths (height, back, arm), widths (shoulder, neck, hip, bust, and waist circumferences). Then, after training a series of models by using SVM (support vector machine) and RBF (radial basis function) kernel function, the recommendation was made for the clothing sizes by selecting the final choices by vote. The accuracy of this model was known to be over 98%. It was explained that in their study, the sizes were recommended with a predictive model where a group of 30 women were trained for the individual clothing sizes of S (155/80A), M (160/84A), L (165/88A) and XL (170/92A) [10]. Another similar study using an SVM-based technique was performed by [PERSON] et al. (2016) who compared a person's body sizes with all the clothing sizes (S, M, L, XL) and calculated the percent deviations of corresponding items, including height, neck, shoulder and waist measurements, to define the clothes having a least F-deviation value as the most suitable one for the recommendation. This method too is possible only when the body sizes of a customer are measured in advance [11]. In fact, size selection can be carried out without developing a complicated predictive model if each item's value is known beforehand; however, what makes recommendation difficult is that when the customer does not know his/her own sizes or cannot measure them on the spot, or it is not possible to put the clothes on him/herself but if individual sizes are available, the sales staff will be able to make a recommendation. The body sizes customers usually know well are height and weight. In the same year (2016), [PERSON] performed an additional study on extracting body sizes from a person's images considering  
Figure 1: A flexible supply chain and digital system. the convenience of online shopping (Figure 2) [12]. He was able to achieve an average of over 91% accuracy by combining the study performed on non-contact size measuring technique that can extract sizes with only the photos taken from front and side views [12]. However, not being able to provide an actual example that can be implemented for the shopping malls was a shortcoming of the study. A method of estimating sizes from available images was once studied by [PERSON], and [PERSON] in 2000 [13] and its scientific significance was that they did not design their concept after anticipating the future emergence of online shopping.