Intelligent Clothing Interaction Design and Evaluation System Based on DCGAN Image Generation Module and Kansei Engineering

Abstract

The deep convolutional generative adversarial network (DCGAN) model can be applied to personalized fashion design. Determining the best design from fashion sketches that is of interest to customers is an important research field. In this study, we propose a rapid evaluation method based on Kansei engineering to evaluate personalized fashion images generated by DCGAN. The degree of customer interest in the generated fashion images is determined by tracking the eye movements and facial expressions of customers. Images with high number of fixations are added to the training data for iterative training; this aims to improving customer satisfaction. The method was verified in an interaction design of personalized black dresses. After several iterations, the customers' satisfaction rating of the generated images by DCGAN improved. This method can be applied to other styles of fashion design.