WiFi-based gesture recognition emerges in recent years and attracts extensive attention from researchers. Recognizing gestures via WiFi signal is feasible because a human gesture introduces a time series of variations to the received raw signal. The major challenge for building a ubiquitous gesture recognition system is that the mapping between each gesture and the series of signal variations is not unique, exact the same gesture but performed at different locations or with different orientations towards the transceivers generates entirely different gesture signals (variations). To remove the location dependency, prior work proposes to use gesture-level location-independent features to characterize the gesture instead of directly matching the signal variation pattern. We observe that gesture-level features cannot fully remove the location dependency since the signal qualities inside each gesture are different and also depends on the location. Therefore, we divide the signal time series of each gesture into segments according to their qualities and propose customized signal processing techniques to handle them separately. To realize this goal, we characterize signal's sensing quality by building a mathematical model that links the gesture signal with the ambient noise, from which we further derive a unique metric i.e., error of dynamic phase index (EDP-index) to quantitatively describe the sensing quality of signal segments of each gesture. We then propose a quality-oriented signal processing framework that maximizes the contribution of the high-quality signal segments and minimizes the impact of low-quality signal segments to improve the performance of gesture recognition applications. We develop a prototype on COTS WiFi devices. The extensive experimental results demonstrate that our system can recognize gestures with an accuracy of more than 94% on average, and significant improvements compared with state-of-arts.