Gender recognition based on multiple scale textural feature

Gou, Jixiang; Gao, Liang; Hou, Peide; Xu, Cunlu

doi:10.1109/cisp.2012.6469817

2012 5th International Congress on Image and Signal Processing 2012

DOI: 10.1109/cisp.2012.6469817

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Gender recognition based on multiple scale textural feature

Jixiang Gou

Liang Gao

Peide Hou

et al.

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2014

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Cited by 1 publication

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Gender Classification Based on Binary Haar Cascade

Yıldırım¹,

Park²,

Song³

et al. 2014

IJCCE

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Gender classification is a difficult but also an essential task under the researches of pattern recognition. There are several methods and features used for this task such as face, gait, or full body features. One of the most widely used techniques is Haar cascades. Default Haar features based classifiers can only detect pedestrian, free from gender information. In this paper we aimed to learn the gender of the target pedestrians by Haar cascades that are trained gender specific. We trained the classifier with only male and female images as positive and negative respectively. Once a basic pedestrian detection has been made over whole image, second detection is made in ROI (Region of Interest) which is the first detected rectangle. Even though we implemented this idea for only pedestrians in this step, it can be applied to other binary problems.

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Gender Classification Based on Binary Haar Cascade

Yıldırım¹,

Park²,

Song³

et al. 2014

IJCCE

View full text Add to dashboard Cite

show abstract

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Gender recognition based on multiple scale textural feature

Cited by 1 publication

References 19 publications

Gender Classification Based on Binary Haar Cascade

Gender Classification Based on Binary Haar Cascade

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