With the increasing prevalence of smart mobile and Internet of things (IoT) environments, user authentication has become a critical component for not only preventing unauthorized access to security-sensitive systems but also providing customized services for individual users. Unlike traditional approaches relying on tedious passwords or specialized biometric/wearable sensors, this paper presents a device-free user authentication via daily human behavioral patterns captured by existing WiFi infrastructures. Specifically, our system exploits readily available channel state information (CSI) in WiFi signals to capture unique behavioral biometrics residing in the user's daily activities, without requiring any dedicated sensors or wearable device attachment. To build such a system, one major challenge is that wireless signals always carry substantial information that is specific to the user's location and surrounding environment, rendering the trained model less effective when being applied to the data collected in a new location or environment. This issue could lead to significant authentication errors and may quickly ruin the whole system in practice. To disentangle the behavioral biometrics for practical environment-independent user authentication, we propose an end-to-end deep-learning based approach with domain adaptation techniques to remove the environment-and location-specific information contained in the collected WiFi measurements. Extensive experiments in a residential apartment and an office with various scales of user location variations and environmental changes demonstrate the effectiveness and generalizability of the proposed authentication system.