In this paper, we propose a method for automatically estimating three typical human-impression factors, "hard-soft", "flashy-sober", and "stable-unstable" which are obtained from objects by analyzing their three-dimensional shapes. By realizing this method, a designer's will in directly shaping an object can be reflected during the design process. Here, the focus is highly correlating human impressions to the three-dimensional shape representation of objects. Previous work includes a method for estimating human impressions by using specially designed features and linear classifiers. However, it can be used for only the "hard-soft" impression factor because the feature has been optimized for this impression. The performance of this method is relatively low, and its processing time is low. In addition to, there is a serious problem in which this method can be used for only a particular impression factor. The purpose of this research is to propose a new method that can apply to all three typical impression factors mentioned above. First, we use a single RGB image that was acquired from a specific view direction instead of general three-dimensional mesh data from the range finder. This enables a very simple system consisting of a single camera. Second, we use a deep neural network as a nonlinear classifier. For our experiment, a lot of learning sample images with numerical human-impression factors were used. As for annotating correct impression factors as ground-truths, we utilized the SD (semantic differential) method, which is very popular in the field of psychological statistics. We have shown that the success rate of the proposed method is 83% for "hard-sofi", 78% for "flashy-sober", and 80% for "stable-unstable" when using test images that are not included in the learning data.