Multi-pose face ensemble classification aided by Gabor features and deep belief nets

A face image with pose variations may be encoded with different representations, which may severely degrade classification performance. In this paper, we consider the problem of classification in the multi-pose face setting using 2D-Gabor features with the deep belief nets approach. We cast the classification as one form of deep learning problem where our goal is to construct the Gabor feature maps in nonlinear characterization. By extracting the 2D-Gabor features of multi-pose faces, then combines Gabor features and the advantages of LTP features; and using of local spatial histogram to describe the face image; then taking X-means algorithm for data processing to further improve the mapping space and dimensionality reduction which can enhances the difference between the data sub-sets. In this way, to learn the complex data space will be automatically divide into multiple sample subspace. We improve the features learning with more discriminating power to benefit the classification problems. We adopt the combination neighborhood component analysis method to mapping this data in a better way. Linear variation of training samples, which can find a more favorable category of linear subspace. We formulate this problem using Gabor feature maps as the input data in deep belief nets. In addition, experimental results on ORL, Yale and LFW datasets show that our proposed algorithm can have better discriminating power and significantly enhance the classification performance, which shows that our algorithm is almost robust to the training samples both on ORL and Yale datasets. Comparisons with eight algorithms (PCA, 2DPCA, Gabor + 2DPCA, SIFT, LBP, LTP, LGBP and LGTP) show that the proposed method is better in recognizing multi-pose faces without large volumes of data. And the experimental results on image classification have verified the effectiveness of the proposed approach.