It is said that the relationship between “novelty” and hedonic response is expressed as an inverse U-shape. The latest studies about perception emphasize “novelty” as a factor of emotion and quantify “novelty” by assessing the difference in amount of information using Kullback-Leibler (KL) divergence. In this study, we proposed a novelty index of closed surfaces using KL divergence focusing on their curvatures. To calculate novelty index, we firstly calculated Gaussian curvature of each vertex in the shape. Then, we defined occurrence probability distribution which represents probability that a vertex has a certain curvature. The KL divergence expresses the difference between the occurrence probability distributions of the standard shape and the target shape. To confirm the effectiveness of the proposed index, we conducted the cognitive experiment using the shape samples of an automobile generated by particle swarm optimization method. The coefficient of determination between the proposed index and sensory evaluation values of “difference” were very high which support the applicability of the index. Furthermore, the consideration of location information increased the correlation with sensory evaluation. This suggests the possibility to evaluate an industrial design requirement quantitatively and contributes to develop the automatic shape generation in product design
