Scalable Face Track Retrieval in Video Archives Using Bag-of-Faces Sparse Representation
Journal
IEEE Transactions on Circuits and Systems for Video Technology
Journal Volume
27
Journal Issue
7
Pages
1595-1603
Date Issued
2017
Author(s)
Abstract
Huge video archives consisting of news programs, dramas, movies, and Web videos (e.g., YouTube) are available in our daily life. In all these videos, human is usually one of the most important subjects. Using state-of-the-art techniques, we can efficiently detect and track faces in the videos. In order to organize large-scale face tracks, containing sequences of (detected) consecutive faces in the videos, we propose an efficient method to retrieve human face tracks using bag-of-faces sparse representation (BoF-SR). Using the proposed method, a face track is encoded as a single BoF-SR, therefore allowing an efficient indexing method to handle large-scale data. To further consider the possible variations in face tracks, we generalize our method to find multiple SRs, in an unsupervised manner, to represent a bag of faces and balance the tradeoff between performance and retrieval time. The experimental results on two real-world (million-scale) data sets confirm that the proposed methods achieve significant performance gains compared with different state-of-the-art methods. ? 1991-2012 IEEE.
Subjects
Bag-of-faces sparse representation (BoF-SR); face track retrieval; multiple SRs
Type
journal article