In recent years, WiFi-based device-free localization (DFL) has attracted attentions due to the rapid development of location-based applications. The localization performance of data-driven DFL models highly relies on the quality of the fingerprint. However, it is difficult to obtain high-quality fingerprints in cluttered environments due to various uncertainties, such as environmental dynamics. To mitigate effects of uncertainties, this article proposes a variance-constrained local–global modeling method to enhance the localization performance. To be specific, the collected channel state information data from a specific environment are first divided into several groups depending on their statistical characteristics using the clustering method, and the local–global modeling mechanism is then implemented based on the extreme learning machine, a kind of noniterative single hidden layer feedforward neural network, to achieve the good representation of the whole environment. During the local–global modeling process, the proposed method is to simultaneously minimize the output weights of the neural network, training errors of the DFL model, and intragroup variances of the grouped data, which makes the created DFL model robust in cluttered environments and not sensitive to uncertainties. Comprehensive experiments in several scenarios, including different indoor environments, device positions and heights, target's orientations, and body shapes, are performed. Experimental results indicate that the proposed method could achieve better localization performance than selected baseline methods, demonstrating the effectiveness of the proposed variance-constrained local–global modeling mechanism in DFL.