Human activity recognition (HAR) is an important task in the field of human-computer interaction. Given the penetration of WiFi devices in our daily lives, HAR using WiFi channel state information (CSI) is a more cost-efficient and comfortable approach. However, most existing approaches ignore the correlation between CSI sub-carriers, which makes their models inefficient and need to rely on deeper and more complex networks to further improve performance. To solve these problems, we propose a reconstructed WiFi CSI phase based HAR approach (WiPhase), which contains a two-stream model to fuse both temporal features and sub-carrier correlation features of reconstructed CSI phase. Specifically, a gated pseudo-Siamese network (GPSiam) is designed to capture the temporal features of the reconstructed sparse CSI phase integration representation (CSI-PIR), and a dynamic resolution based graph attention network (DRGAT) is designed to capture the nonlinear correlation between CSI sub-carriers by the reconstructed CSI phase graph. Furthermore, dendrite network (DD) makes the final decision by combining the features output from GPSiam and DRGAT. Experimental results show that WiPhase outperforms the existing state-of-the-art approaches.