Face recognition based on stretchy regression

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, an asymmetric kernel is proposed for extracting sparse features from two-dimensional visual face images for identity recognition. Essentially, the kernel consists of an inner product of two vectors where one of them has been raised to power terms element-wise. The impact of such a power term is suppression of less influential features where only relevant ones are used for estimation. Our experiments on public data sets show encouraging results regarding the potential of such an asymmetric kernel.

Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages819-824
Number of pages6
ISBN (Electronic)9781509026050
DOIs
Publication statusPublished - 2016 Oct 19
Event11th IEEE Conference on Industrial Electronics and Applications, ICIEA 2016 - Hefei, China
Duration: 2016 Jun 52016 Jun 7

Publication series

NameProceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016

Other

Other11th IEEE Conference on Industrial Electronics and Applications, ICIEA 2016
CountryChina
CityHefei
Period16/6/516/6/7

Bibliographical note

Funding Information:
The author thanks Zhiping Lin (NTU), Zhengguo Li (I2R) and Lei Sun (BIT) for valuable discussions along the main direction of stretchy learning. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant number: NRF- 2015R1D1A1A09061316)

Publisher Copyright:
© 2016 IEEE.

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Control and Optimization

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