In this paper, we present a system for person re-identification in movies and dramas. Our person re-identification method simultaneously learns differences and commonalities between two pairs of images. Features are extracted using three images, and the relationship between the extracted features is constructed into two pairs of feature maps to learn. Our method significantly outperforms on a large data set (CUHK03), and our own re-identification dataset generated from video contents such as movies and dramas. We also show that the proposed model learned on our own data set can be applied to actor re-identification in video content.
KSP Keywords
Feature map, Large datasets, Person Re-Identification, Proposed model, Video content, identification method
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