We present a system for accurate real-time 3D face verification using a low-quality consumer depth camera. To verify the identity of a subject, we built a high-quality reference model offline by fitting a 3D morphable model to a sequence of low-quality depth images. At runtime, we compare the similarity between the reference model and a single depth image by aligning the model to the image and measuring differences between every point on the two facial surfaces. The model and the image will not match exactly due to sensor noise, occlusions, as well as changes in expression, hairstyle, and eye-wear; therefore, we leverage a data driven approach to determine whether or not the model and the image match. We train a random decision forest to verify the identity of a subject where the point-to-point distances between the reference model and the depth image are used as input features to the classifier. Our approach runs in real-time and is designed to continuously authenticate a user as he/she uses his/her device. In addition, our proposed method outperforms existing 2D and 3D face verification methods on a benchmark data set.