A deep graph embedding network model for face recognition

In this paper, we propose a new deep learning network “GENet”, it combines the multi-layer network architecture and graph embedding framework. Firstly, we use simplest unsupervised learning PCA/LDA as first layer to generate the low-level feature. Secondly, many cascaded dimensionality reduction layers based on graph embedding framework are applied to GENet. Finally, a linear SVM classifier is used to classify dimension-reduced features. The experiments indicate that higher classification accuracy can be obtained by this algorithm on the CMU-PIE, ORL, Extended Yale B dataset.