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Deep Video Code for Efficient Face Video Retrieval

  • Shishi Qiao
  • Ruiping WangEmail author
  • Shiguang Shan
  • Xilin Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10113)

Abstract

In this paper, we address the problem of face video retrieval. Given one face video of a person as query, we search the database and return the most relevant face videos, i.e., ones have same class label with the query. Such problem is of great challenge. For one thing, faces in videos have large intra-class variations. For another, it is a retrieval task which has high request on efficiency of space and time. To handle such challenges, this paper proposes a novel Deep Video Code (DVC) method which encodes face videos into compact binary codes. Specifically, we devise a multi-branch CNN architecture that takes face videos as training inputs, models each of them as a unified representation by temporal feature pooling operation, and finally projects the high-dimensional representations into Hamming space to generate a single binary code for each video as output, where distance of dissimilar pairs is larger than that of similar pairs by a margin. To this end, a smooth upper bound on triplet loss function which can avoid bad local optimal solution is elaborately designed to preserve relative similarity among face videos in the output space. Extensive experiments with comparison to the state-of-the-arts verify the effectiveness of our method.

Keywords

Binary Code Video Modeling Locality Sensitive Hashing Video Representation Video Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work is partially supported by 973 Program under contract No. 2015CB351802, Natural Science Foundation of China under contracts Nos. 61390511, 61379083, 61272321, and Youth Innovation Promotion Association CAS No. 2015085.

Supplementary material

416261_1_En_20_MOESM1_ESM.pdf (180 kb)
Supplementary material 1 (pdf 180 KB)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shishi Qiao
    • 1
    • 2
  • Ruiping Wang
    • 1
    • 2
    • 3
    Email author
  • Shiguang Shan
    • 1
    • 2
    • 3
  • Xilin Chen
    • 1
    • 2
    • 3
  1. 1.Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS)Institute of Computing Technology, CASBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Cooperative Medianet Innovation CenterBeijingChina

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