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 language | English |
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| Title of host publication | Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 819-824 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781509026050 |
| DOIs | |
| Publication status | Published - 2016 Oct 19 |
| Event | 11th IEEE Conference on Industrial Electronics and Applications, ICIEA 2016 - Hefei, China Duration: 2016 Jun 5 → 2016 Jun 7 |
Publication series
| Name | Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016 |
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Other
| Other | 11th IEEE Conference on Industrial Electronics and Applications, ICIEA 2016 |
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| Country/Territory | China |
| City | Hefei |
| Period | 16/6/5 → 16/6/7 |
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