@inproceedings{6a7dba38d3804e6c89bdbd451ac76a9c,
title = "Cleaning up after a face tracker: False positive removal",
abstract = "Automatic person identification in TV series has gained popularity over the years. While most of the works rely on using face-based recognition, errors during tracking such as false positive face tracks are typically ignored. We propose a variety of methods to remove false positive face tracks and categorize the methods into confidence- and context-based. We evaluate our methods on a large TV series data set and show that up to 75% of the false positive face tracks are removed at the cost of 3.6% true positive tracks. We further show that the proposed method is general and applicable to other detectors or trackers.",
keywords = "face tracking, false positive removal, TV series, video processing",
author = "Makarand Tapaswi and {\c C}{\"o}rez, {Cemal {\c C}aʇi} and Martin B{\"a}uml and Ekenel, {Hazim Kemal} and Rainer Stiefelhagen",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.",
year = "2014",
month = jan,
day = "28",
doi = "10.1109/ICIP.2014.7025050",
language = "English (US)",
series = "2014 IEEE International Conference on Image Processing, ICIP 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "253--257",
booktitle = "2014 IEEE International Conference on Image Processing, ICIP 2014",
}