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In the past few years, there has been a dramatic growth in e-manga(electronic Japanese-style comics). Faced with the booming demand for mangaresearch and the large amount of unlabeled manga data, we raised a new task,called unsupervised manga character re-identification. However, the artisticexpression and stylistic limitations of manga pose many challenges to there-identification problem. Inspired by the idea that some content-relatedfeatures may help clustering, we propose a Face-body and Spatial-temporalAssociated Clustering method (FSAC). In the face-body combination module, aface-body graph is constructed to solve problems such as exaggeration anddeformation in artistic creation by using the integrity of the image. In thespatial-temporal relationship correction module, we analyze the appearancefeatures of characters and design a temporal-spatial-related triplet loss tofine-tune the clustering. Extensive experiments on a manga book dataset with109 volumes validate the superiority of our method in unsupervised mangacharacter re-identification.

Unsupervised Manga Character Re-identification via Face-body and Spatial-temporal Associated Clustering