Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6864960 | Neurocomputing | 2018 | 20 Pages |
Abstract
Subspace learning approaches extract features by a simple linear transformation, which can viewed as a shallow network, and they cannot reveal the deep structure embedded in pixels of image. To solve this problem, a deep principal component analysis (PCA) network, namely enhanced PCA Network (EPCANet), is proposed to explore more distinctive representation for face images. EPCANet adds a spatial pooling layer between the first layer and second layer in the PCANet. The spatial pooling layer reveals more spatial and distinctive information by down-sampling or pixel offset for the first layer output and original images. Extensive experimental results in several databases illustrate the efficiency of our proposed methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Liu Yang, Zhao Shuangshuang, Wang Qianqian, Gao Quanxue,