The cornea in the human eye reflects incoming environmental light, which means we can obtain information about the surrounding environment from the corneal reflection in facial images. In recent years, as the quality of consumer cameras increases, this has caused privacy concerns in terms of identifying the people around the subject or where the photo is taken. This paper investigates the security risk of eye corneal reflection images: specifically, visual place recognition from eye reflection images. First, we constructed two datasets containing pairs of scene and corneal reflection images. The first dataset is taken in a virtual environment. We showed pre-captured scene images in a 180-degree surrounding display system and took corneal reflections from subjects. The second dataset is taken in an outdoor environment. We developed several visual place recognition algorithms, including CNN-based image descriptors featuring a naive Siamese network and AFD-Net combined with entire image feature representations including VLAD and NetVLAD, and compared the results. We found that AFD-Net+VLAD performed the best and was able to accurately determine the scene in 73.08% of the top-five candidate scenes. These results demonstrate the potential to estimate the location at which a facial picture was taken, which simultaneously leads to a) positive applications such as the localization of a robot while conversing with persons and b) negative scenarios including the security risk of uploading facial images to the public.