The Principal Component Analysis Network (PCANet) is a kind of Deep subspace network based on the simplified architecture of neural network. To address the issue that PCANet cannot process samples in real-time during the convolutional kernel extraction process, this article proposed an Incremental sequential row-column 2DPCA network (IRC2DPCANet). This method can process train samples on time in the process of filter training, which can improve the efficiency of network training. The experiments on three typical datasets, which is PIE, AR and Yale, indicate that this method has good classification performance. In this article, the influence of the filter number and filter size on classification rate is also investigated.