In this paper, we propose using image recognition techniques to estimate the “understanding measure” in person-toperson teaching situations. The phrase “understanding measure” refers to how strongly a teacher feels a student understands a topic. First, we extract a student’s nonverbal behavior (head movement, gazes, and blinking) as the features for the estimation process. Next, we calculate the subspace from the aforementioned feature by using principal component analysis (PCA) and linear discriminant analysis (LDA). Finally, we classify unknown data as either “understood” or “did not understand” by using a kNN classifier in subspace. Our experiments confirmed that the Fmeasure of the classification “understood” by our method was 0.75 and “did not understand” was 0.60, indicating that our method improved F-measures 0.38 and 0.11, respectively, compared with previous methods.