Face Detection on Pre-modern Japanese Artworks using R-CNN and Image Patching for Semi-Automatic Annotation

Abstract

We propose a face detection method for semi-Automatic annotation of faces on pre-modern Japanese artworks to assist art historians identify objects in the art collection. Our method is based on R-CNN, such as Faster R-CNN and Cascade R-CNN, for object detection, and image patching for taking advantage of high resolution images. Our face detectors were first trained on the KaoKore dataset to demonstrate that existing object detection models with image patching can successfully learn faces in artworks. Our face detectors were then applied to the Kouhon dataset to assist art historians create a new facial expression dataset. Finally the impact of face detection on art history research was measured by the reduction of annotation time, and it was estimated to be $1/3$ in comparison to fully manualdiscussed as the reduction of annotation time to $1/3$ in comparison to fully manual annotation.