@inproceedings{6790c8b9896949cfb50dc6cb5fd912d6,
title = "New benchmark datasets and a character identification system on TV series",
abstract = "Automatic character identification has been extensively studied in the past decades. However, most related works find characters only depending on their faces. One important reason is no dataset containing labels with all the occurrences of characters. Towards this end, we propose 3 datasets for character identification, which consist of 15 episodes (573 minutes). We label all the frames in which specified characters can be identified manually, regardless of whether faces or persons are detected. To the best of our knowledge, they are the first and largest datasets with all the occurrences of specified characters labelled for automatic character identification on TV material. Based on these datasets, we propose an automatic character identification system. Given a query image with a clear frontal face of a character, we can effectively utilize identified frames as new queries to explore remaining ones. In experiments, we show the average precision is substantially boosted in many cases.",
keywords = "Identification, Re-ranking, TV, Track",
author = "Zhuo Lei and Qian Zhang and Guoping Qiu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019 ; Conference date: 08-07-2019 Through 12-07-2019",
year = "2019",
month = jul,
doi = "10.1109/ICMEW.2019.00106",
language = "English",
series = "Proceedings - 2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "585--590",
booktitle = "Proceedings - 2019 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2019",
address = "United States",
}