Multi-pose Face Classification Based on the 2D-Gabor features and Deep Belief Nets
Yong Chen · Bandaoti guangdian · 2015
Aiming at the pose problems of face image which may severely degrade the classification performance,proposed was a multi-pose face classification method based on the 2DGabor features and deep belief nets(DBNs)approach to construct deep learning for classifying multi-pose images accurately.By extracting 2D-Gabor features of multi-pose faces and fusing them,it is aimed to improve the features learning with more discriminating power to benefit the classification problems.By using fused images as the input image in deep belief nets and incorporating the neighborhood component analysis,it is to change the linearly training samples so as to find a more favorable category of linear subspace,then we can provide a large enough data set can be provided to estimate model parameters.The average classification accuracies of the proposed algorithm on the ORL images datasets are 86.67%、84.00%、90.67% and 86.67%respectively when the multi-pose face classified data is between 16×16 and 24×32.The classification accuracy is improeved compared to the PCA、LDA and RCA methods.Experimental results verifies the effectiveness of proposede algorithm.