In this paper, we propose a new deep learning network "GENet", it combines the multi-layer network architec- ture 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.
@article{arxiv.1409.7313,
title = {A Deep Graph Embedding Network Model for Face Recognition},
author = {Yufei Gan and Teng Yang and Chu He},
journal= {arXiv preprint arXiv:1409.7313},
year = {2014}
}