Towards better fitting clothes: AI-supported morphological classification of body scans
It is still a challenge for the apparel industry to develop good fitting products and underlying sizing and grading systems.
This is due to the diversity of human bodies having the same traditional size but different morphotypes.
Additional reasons are differences between different countries and special target groups such as young people or old people. The objective of the presented iMorph approach is the morphological classification based on body scan data to be used for size system development and to provide better fitting clothes.
Additional applications include recommendations systems in online business and curated shopping. iMorph is a unique approach to estimate the morphological classification of individuals based on body scan data.
First, a morphological classification scheme was developed. It comprises 10 features and according ordinal scales.
Next, human experts visually classified a number of selected scans (data sets) by looking at the scanatars.
The resulting case base of classified scans is the core of a Case-Based Reasoning (CBR) system.
It is able to automatically classify huge numbers of scans based on a similarity model. In addition, artificial neural networks (ANN) have been applied to classify the scans.
Both methods have their advantages but lead to similar results.