End-To-End Face Detection and Cast Grouping in Movies Using Erdos-Renyi Clustering

SouYoung Jin, Hang Su, Chris Stauffer, Erik Learned-Miller; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 5276-5285

Abstract


We present an end-to-end system for detecting and clustering faces by identity in full-length movies. Unlike works that start with a predefined set of detected faces, we consider the end-to-end problem of detection and clustering together. We make three separate contributions. First, we combine a state-of-the-art face detector with a generic tracker to extract high quality face tracklets. We then introduce a novel clustering method, motivated by the classic graph theory results of Erdos and Renyi. It is based on the observations that large clusters can be fully connected by joining just a small fraction of their point pairs, while just a single connection between two different people can lead to poor clustering results. This suggests clustering using a verification system with very few false positives but perhaps moderate recall. We introduce a novel verification method, rank-1 counts verification, that has this property, and use it in a link-based clustering scheme. Finally, we define a novel end-to-end detection and clustering evaluation metric allowing us to assess the accuracy of the entire end-to-end system. We present state-of-the-art results on multiple video data sets and also on standard face databases.

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[bibtex]
@InProceedings{Jin_2017_ICCV,
author = {Jin, SouYoung and Su, Hang and Stauffer, Chris and Learned-Miller, Erik},
title = {End-To-End Face Detection and Cast Grouping in Movies Using Erdos-Renyi Clustering},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}