A statistic manifold kernel with graph embedding discriminant analysis for action and expression recognition

Shuanglu Dai, Hong Man

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Graph embedding discriminant analysis is effective but computationally expensive for video-based recognition tasks. This paper proposes a statistic manifold kernel for visual modeling. Discriminant analysis can achieve effective computation with the proposed kernel for action and expression recognition. Firstly, symmetric positive definite (SPD) manifold is proposed to incorporate Gaussian mixture distribution of the video clips. Secondly, a projection kernel is constructed on the SPD manifold. Then an inter-class graph and an intra-class graph are introduced to measure the inter-class separability and intra-class compactness. The geometrical structure of the input data is thus exploited. A Marginal discriminant analysis(MDA) is finally performed on the kernel Hilbert space of the SPD Riemannian manifold. Recognition is achieved by the Nearest Neighbor (NN) method. Promising performances demonstrate the effectiveness of the proposed method for action and facial expression recognition.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
Pages1792-1796
Number of pages5
ISBN (Electronic)9781509021758
DOIs
StatePublished - 2 Jul 2017
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: 17 Sep 201720 Sep 2017

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2017-September
ISSN (Print)1522-4880

Conference

Conference24th IEEE International Conference on Image Processing, ICIP 2017
Country/TerritoryChina
CityBeijing
Period17/09/1720/09/17

Keywords

  • Action recognition
  • Facial expression recognition
  • Graph embedding
  • Marginal discriminant analysis
  • Statistic manifold kernel

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