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13

ICPR
2006
IEEE

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An Experimental Study on Automatic Face Gender Classification

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An Experimental Study on Automatic Face Gender Classification

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This paper presents an experimental study on automatic face gender classification by building a system that mainly consists of four parts, face detection, face alignment, texture normalization and gender classification. Comparative study on the effects of different texture normalization methods including two kinds of affine mapping and one Delaunay triangulation based warping as preprocesses for gender classification by SVM, LDA and Real Adaboost respectively is reported through experiments on very large sets of snapshot images.

Haizhou Ai, Ming Li, Zhiguang Yang

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Computer Vision | Face Alignment | Face Gender Classification | ICPR 2006 | Texture Normalization Methods |

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Added	09 Nov 2009
Updated	09 Nov 2009
Type	Conference
Year	2006
Where	ICPR
Authors	Haizhou Ai, Ming Li, Zhiguang Yang

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