Computer Science ›› 2018, Vol. 45 ›› Issue (10): 267-271.doi: 10.11896/j.issn.1002-137X.2018.10.049

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

Method of Face Recognition and Dimension Reduction Based on Curv-SAE Feature Fusion

ZHANG Zhi-yu, LIU Si-yuan   

  1. School of Automation and Information Engineering,Xi’an University of Technology,Xi’an 710048,China
  • Received:2017-09-17 Online:2018-11-05 Published:2018-11-05

Abstract: Compared with the traditional dimension reduction algorithm,stacked autoencoders (SAE)in deep learning can effectively learn the features and achieve efficient dimension reduction,but its performance depends on the input characteristics.The second generation discrete curvelet transform can extract the information of human faces,including edge and overview features,and ensure that the input features of SAE are sufficient,thus making up for the shortages of SAE.Therefore,a new recognition and dimension reduction algorithm based on Curv-SAE feature fusion was proposed.Firstly,the face images are processed by DCT to generate the Curv-faces,which are trained as input characteristics of SAE.And then different layers of features are used for the final classification of identification.Experimental results on ORL and FERET face databases show that the feature information of curvelet transform is more abundant than the wavelet transform.Compared with the traditional dimension reduction algorithms,the feature expression of SAE is more complete and the recognition accuracy is higher.

Key words: Deep learning, Face recognition, The Second generation discrete curvelet transform, Stacked autoencoders, Dimension reduction

CLC Number: 

  • TP391.41
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