Fuzzy Evaluation of Kansei Attributes Using Convolutional Neural Networks

Abstract The emotional needs for products have increased significantly with the recent improvements in living standards. Attribute evaluation forms the core of Kansei engineering in emotion-oriented products, and is practically quite subjective in nature. Essentially, attribute evaluation is a fuzzy classification task, whose quantitative results change slightly with statistical time and statistical objects, making it difficult to accurately describe using standard mathematical models. In this paper, we propose a novel deep-learning-assisted fuzzy attribute-evaluation (DLFAE) method, which could generate quantitative evaluation results. In comparison to existing methods, the proposed method combines subjective evaluation with convolutional neural networks, which facilitates the generation of quantitative evaluation results. Additionally, this strategy has better transferability for different situations, increasing its versatility and applicability. This, in turn, reduces the computational burden of evaluation and improves operational efficiency.