We present a pattern watermarking framework for system that captures the 3D shape of face, in order to support face dependent applications e.g. face identification. As the face may move during the 3D capture, it is important that the shape is retrieved within as short a time as possible e.g. stereo, one-shot structured light. On the other hand, the face needs to be captured from multiple sides. In order to get a fast, full-face capture without compromised resolution on profile sides. We have devised a method to let multiple projectors and cameras work simultaneously. A projected pattern in combination with a camera allows for a structured light approach. This is beneficial, given the weakly textured surfaces we are dealing with. Yet, where projection patterns overlap, our system automatically changes over to a multi-view approach. In order to let the system automatically detect whether a single projection vs. an overlap is observed, we watermark the different projection patterns while preserving enough textures for correspondence match. In our system, two projectors and two cameras are deployed. Each camera-project pair consists a one-shot structured light set and the two cameras consist a multiview stereo. Our method is fully compatible with now-a-days tensor computation platforms, which provides simplicity for research and development as well as easy-extendability for industrial application and running-time performance optimization. This paper presents watermarking patterns and corresponding detection methods, in tensor computation.