Gender Classification Based on Boosting Local Binary Pattern

  title={Gender Classification Based on Boosting Local Binary Pattern},
  author={Ning Sun and Wenming Zheng and Changyin Sun and Cairong Zou and Lu Zhao},
This paper presents a novel approach for gender classification by boosting local binary pattern-based classifiers. The face area is scanned with scalable small windows from which Local Binary Pattern (LBP) histograms are obtained to effectively express the local feature of a face image. The Chi square distance between corresponding Local Binary Pattern histograms of sample image and template is used to construct weak classifiers pool. Adaboost algorithm is applied to build the final strong… CONTINUE READING
Highly Influential
This paper has highly influenced 11 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 150 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 90 extracted citations

150 Citations

Citations per Year
Semantic Scholar estimates that this publication has 150 citations based on the available data.

See our FAQ for additional information.

Similar Papers

Loading similar papers…