Face is a highly utilized biometric, and 3D modality is preferred due to better handling of variations such as pose and illumination. However, occlusions covering the face alter the 3D surface and degrade the recognition performance. To improve recognition rates, the occluded parts should be detected prior to any surface comparison. In this paper, we consider two different occlusion detection approaches: The first one is based on statistical facial surface modeling, where pixel-wise Gaussian Mixture Models are trained. The second algorithm considers occlusion detection as a binary image segmentation problem: The regional cues of depth values are incorporated with neighborhood cues, and the acquired surface is modeled as a graph. The surface pixels are labeled as either face or occlusion via the graph cut technique. Experiments on the Bosphorus and the UMB-DB databases, including realistic occlusion variations, show that both methods improve occlusion detection and face recognition rates as compared to the baseline technique.