Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 247-251.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

Face Recognition Using 2D Gabor Feature and 3D NP-3DHOG Feature

WANG Xue-qiao,QI Hua-shan, YUAN Jia-zheng, LIANG Ai-hua, SUN Li-hong   

  1. Beijing Union University,Beijing 100101,China
  • Online:2018-06-20 Published:2018-08-03

Abstract: Face recognition algorithm based on 2D images extracts texture feature for recognition,but lighting,facial expressions and facial gestures can have adverse effect on it.3D face features can accurately describe the geometric structure of face and they are barely affected by makeup and light.Because 3D face feature lacks texture information,two kinds of features for face recognition.This paper fused Gabor based 2D face feature and new partitioning 3D histograms of oriented gradients 3D feature for face recognition.Firstly,the Gabor feature of 2D face is extracted,then the new partitioning 3D histograms of oriented gradients feature are extracted,which aims to extract the discriminant 3D face feature.Secondly,the linear discriminant analysis subspace algorithm is used to train two subspaces respectively.Finally,sum rule is used to fuse the two similarity matrices,and the nearest neighbor classifier is applied to finish the recognition process.

Key words: 2D texture feature, 3D discriminant feature, Face recognition, Feature fusion, Histograms of oriented gradients

CLC Number: 

  • TP242.6
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