Hyppää sisältöön
    • FI
    • ENG
  • FI
  • /
  • EN
OuluREPO – Oulun yliopiston julkaisuarkisto / University of Oulu repository
Näytä viite 
  •   OuluREPO etusivu
  • Oulun yliopisto
  • Avoin saatavuus
  • Näytä viite
  •   OuluREPO etusivu
  • Oulun yliopisto
  • Avoin saatavuus
  • Näytä viite
JavaScript is disabled for your browser. Some features of this site may not work without it.

Learning from hierarchical spatiotemporal descriptors for micro-expression recognition

Zong, Yuan; Huang, Xiaohua; Zheng, Wenming; Cui, Zhen; Zhao , Guoying (2018-03-30)

 
Avaa tiedosto
nbnfi-fe2018112348954.pdf (3.287Mt)
nbnfi-fe2018112348954_meta.xml (37.38Kt)
nbnfi-fe2018112348954_solr.xml (41.97Kt)
Lataukset: 

URL:
https://doi.org/10.1109/TMM.2018.2820321

Zong, Yuan
Huang, Xiaohua
Zheng, Wenming
Cui, Zhen
Zhao , Guoying
Institute of Electrical and Electronics Engineers
30.03.2018

Y. Zong, X. Huang, W. Zheng, Z. Cui and G. Zhao, "Learning From Hierarchical Spatiotemporal Descriptors for Micro-Expression Recognition," in IEEE Transactions on Multimedia, vol. 20, no. 11, pp. 3160-3172, Nov. 2018. doi: 10.1109/TMM.2018.2820321

https://rightsstatements.org/vocab/InC/1.0/
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1109/TMM.2018.2820321
Näytä kaikki kuvailutiedot
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2018112348954
Tiivistelmä

Abstract

Micro-expression recognition aims to infer genuine emotions that people try to conceal from facial video clips. It is a very challenging task because micro-expressions have a very low intensity and short duration, which makes micro-expressions difficult to observe. Recently, researchers have designed various spatiotemporal descriptors to describe micro-expressions. It is notable that for better capturing the low-intensity facial muscle movement, a fixed spatial division grid, 8× 8 for example, is commonly used to partition the facial images into a few facial blocks before extracting descriptors. However, it is hard to choose an ideal division grid for different micro-expression samples because the division grids affect the discriminative ability of spatiotemporal descriptors to distinguish micro-expressions. To address this problem, in this paper, we design a hierarchical spatial division scheme for spatiotemporal descriptor extraction. By using the proposed scheme, it would not be a problem to determine which division grid is most suitable regarding different micro-expression samples. Furthermore, we propose a kernelized group sparse learning (KGSL) model to process hierarchical scheme based spatiotemporal descriptors such that they are more effective for micro-expression recognition tasks. To evaluate the performance of the proposed micro-expression recognition method consisting of the hierarchical scheme based spatiotemporal descriptors and KGSL, extensive experiments are conducted on two public micro-expression databases: CASME II and SMIC. Compared with many recent state-of-the-art approaches, our method achieves more promising recognition results.

Kokoelmat
  • Avoin saatavuus [37559]
oulurepo@oulu.fiOulun yliopiston kirjastoOuluCRISLaturiMuuntaja
SaavutettavuusselosteTietosuojailmoitusYlläpidon kirjautuminen
 

Selaa kokoelmaa

NimekkeetTekijätJulkaisuajatAsiasanatUusimmatSivukartta

Omat tiedot

Kirjaudu sisäänRekisteröidy
oulurepo@oulu.fiOulun yliopiston kirjastoOuluCRISLaturiMuuntaja
SaavutettavuusselosteTietosuojailmoitusYlläpidon kirjautuminen