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2018 | OriginalPaper | Chapter

Fusing Appearance Features and Correlation Features for Face Video Retrieval

Authors : Chenchen Jing, Zhen Dong, Mingtao Pei, Yunde Jia

Published in: Advances in Multimedia Information Processing – PCM 2017

Publisher: Springer International Publishing

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Abstract

Face video retrieval has drawn considerable research attention recently. Most prior research mainly focused on either appearance features or correlation features, which could degrade retrieval performance. In this paper, we fuse appearance features and correlation features to exploit rich information of face videos for face video retrieval via a deep convolutional neural network. The network extracts appearance feature and correlation feature from a frame and the covariance matrix of a face video, respectively, and fuses them to obtain a comprehensive video representation. The fused feature is projected to a low-dimensional Hamming space via hash functions for the retrieval task. The network integrates feature extractions, feature fusion, and hash learning into a unified optimization framework to guarantee optimal compatibility of appearance features and correlation features. Experiments on two challenging TV-Series datasets demonstrate the effectiveness of the proposed method.

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Metadata
Title
Fusing Appearance Features and Correlation Features for Face Video Retrieval
Authors
Chenchen Jing
Zhen Dong
Mingtao Pei
Yunde Jia
Copyright Year
2018
Book
Advances in Multimedia Information Processing – PCM 2017

Print ISBN: 978-3-319-77382-7

Electronic ISBN: 978-3-319-77383-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-77383-4_15