In the traditional Japanese (Kampo) medicine, diagnosis is based on the observation of facial color and expression. Disease states that are diagnosable from facial characteristics include blood stagnation, blood deficiency, and yin deficiency. However, these diagnoses are subjective and require lots of experience. In this paper, we aimed to quantify facial diagnosis by creating a system to evaluate the severity of disease states using feature values obtained from facial images. A Kampo physician evaluated the facial images and rated them from 1 to 5 according to severity of disease states. We verified the accuracy of this system using the mean squared error calculated from the difference between the physician evaluation scores and the system estimates. The mean squared error was close to zero, indicating that the system has high accuracy. The selection of feature values using this system corresponded with those facial regions used by the Kampo physician in diagnosing the disease states.

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3D reconstruction is used for inspection of industrial products. The demand for measuring 3D shapes is increased. There are many methods for 3D reconstruction using RGB images. However, it is difficult to reconstruct 3D shape using RGB images with gloss. In this paper, we use the deep neural network to remove the gloss from the image group captured by the RGB camera, and reconstruct the 3D shape with high accuracy than conventional method. In order to do the evaluation experiment, we use CG of simple shape and create images which changed geometry such as illumination direction. We removed gloss on these images and corrected defect parts after gloss removal for accurately estimating 3D shape. Finally, we compared 3D estimation using proposed method and conventional method by photo metric stereo. As a result, we show that the proposed method can estimate 3D shape more accurately than the conventional method.