Noise tolerant histogram voting for gender Classification Based on LBP

Sanghun Lee, Chulhee Lee

Research output: Contribution to journalConference article

Abstract

In this paper, we present a noise tolerant descriptor based on a local binary pattern (LBP) method. Due to threshold-based operations, these types of LBP methods are sensitive to noise factors. The use of a robust LBP (RLBP) reduced some noise effects. However, it may lead to a loss of subtle local texture information. Instead of concatenating the LBP and RLBP features, we produced a histogram as a weighted sum of the histograms of the LBPs and the RLBP. The proposed noise tolerant LBP (NTLBP) was calculated using the LBP histogram and histogram voting results of the RLBP. Without increasing the number of features, NTLBP proved to be robust against noise effects. We conducted several gender classification experiments using the FERET database and the NTLBP outperformed both the LBP and the RLBP methods.

Original languageEnglish
JournalIS and T International Symposium on Electronic Imaging Science and Technology
DOIs
Publication statusPublished - 2016 Jan 1
EventImage Processing: Machine Vision Applications IX 2016 - San Francisco, United States
Duration: 2016 Feb 142016 Feb 18

Fingerprint

voting
histograms
Textures
Experiments
textures
thresholds

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Human-Computer Interaction
  • Software
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics

Cite this

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abstract = "In this paper, we present a noise tolerant descriptor based on a local binary pattern (LBP) method. Due to threshold-based operations, these types of LBP methods are sensitive to noise factors. The use of a robust LBP (RLBP) reduced some noise effects. However, it may lead to a loss of subtle local texture information. Instead of concatenating the LBP and RLBP features, we produced a histogram as a weighted sum of the histograms of the LBPs and the RLBP. The proposed noise tolerant LBP (NTLBP) was calculated using the LBP histogram and histogram voting results of the RLBP. Without increasing the number of features, NTLBP proved to be robust against noise effects. We conducted several gender classification experiments using the FERET database and the NTLBP outperformed both the LBP and the RLBP methods.",
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