Stacked PCA Network (SPCANet): An effective deep learning for face recognition
Lei Tian, Chunxiao Fan, Yue Ming, Yi Jin · 2015
High-level features can represent the semantics of the original data and it is a plausible way to avoid the problem of hand-crafted features for face recognition. This paper proposes an effective deep learning framework by stacking multiple output features that learned through each stage of the Convolutional Neural Network (CNN). Different from the traditional deep learning network, we use Principal Component Analysis (PCA) to get the filter kernels of convolutional layer, which is name as Stacked PCA Network (SPCANet). Our SPCANet model follows the basic architecture of the CNN, which comprises three layers in each stage: convolutional filter layer, nonlinear processing layer and feature pooling layer. Firstly, in the convolutional filter layer of our model, PCA instead of stochastic gradient descent (SGD) is employed to learn filter kernels, and the output of all cascaded convolutional filter layers is used as the input of nonlinear processing layer. Secondly, the following nonlinear processing layer is also simplified. We use hashing method for nonlinear processing. Thirdly, the block based histograms instead of max-pooling technique are employed in the feature pooling layer. In the last output layer, the output of each stage is stacked together as one final feature output of our model. Extensive experiments conducted on many different face recognition scenarios demonstrate the effectiveness of our proposed approach.